diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..98f9a1a5aeeb316fa9aa31866aa90ed22f56ca12 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+wandb/run-20260421_160121-lnmebn7s/run-lnmebn7s.wandb filter=lfs diff=lfs merge=lfs -text
+wandb/run-20260421_165557-ngbvlof1/run-ngbvlof1.wandb filter=lfs diff=lfs merge=lfs -text
diff --git a/.python-version b/.python-version
new file mode 100644
index 0000000000000000000000000000000000000000..11aaa06863cbf9efa1499447a9ac9e4d71ee8073
--- /dev/null
+++ b/.python-version
@@ -0,0 +1 @@
+3.9.5
diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000000000000000000000000000000000000..8db27d520af7be731b6bc8c79a63d53084365ecc
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,10 @@
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+    "editor.codeActionsOnSave": {
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+    }
+}
\ No newline at end of file
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000000000000000000000000000000000000..aac1baa3001e5c02d99e55feaf0cb103c4c23c56
--- /dev/null
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+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+propcache = ">=0.2.1"
+
+[metadata]
+lock-version = "2.0"
+python-versions = "~3.10"
+content-hash = "b68659a39c33c0fac55148c404ac7497de19bf8b98de0fae25af8bf2ebe3f73e"
diff --git a/ppo-LunarLander-v2.pt b/ppo-LunarLander-v2.pt
new file mode 100644
index 0000000000000000000000000000000000000000..0501916ae2b30666a4c2540c6250fe520a6bdb5a
--- /dev/null
+++ b/ppo-LunarLander-v2.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ae83d259ed07741805748cf65c6a7c0b1279ccf2fa154cac1d305453e0becfc8
+size 44165
diff --git a/ppo.py b/ppo.py
new file mode 100644
index 0000000000000000000000000000000000000000..494b1766012b205368510f87ef0dd558feb913e7
--- /dev/null
+++ b/ppo.py
@@ -0,0 +1,133 @@
+import os
+
+import gymnasium as gym
+import numpy as np  # Ensure numpy is imported
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.distributions.categorical import Categorical
+
+# --- Hyperparameters ---
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+env_id = "LunarLander-v2"
+total_timesteps = 500000
+learning_rate = 2.5e-4
+num_envs = 4
+num_steps = 128
+batch_size = num_envs * num_steps
+minibatch_size = 32
+update_epochs = 4
+clip_coef = 0.2
+ent_coef = 0.01
+
+
+# --- Model Architecture ---
+def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
+    torch.nn.init.orthogonal_(layer.weight, std)
+    torch.nn.init.constant_(layer.bias, bias_const)
+    return layer
+
+
+class Agent(nn.Module):
+    def __init__(self, envs):
+        super().__init__()
+        self.critic = nn.Sequential(
+            layer_init(
+                nn.Linear(
+                    np.array(envs.single_observation_space.shape).prod(), 64
+                )
+            ),
+            nn.Tanh(),
+            layer_init(nn.Linear(64, 64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64, 1), std=1.0),
+        )
+        self.actor = nn.Sequential(
+            layer_init(
+                nn.Linear(
+                    np.array(envs.single_observation_space.shape).prod(), 64
+                )
+            ),
+            nn.Tanh(),
+            layer_init(nn.Linear(64, 64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64, envs.single_action_space.n), std=0.01),
+        )
+
+    def get_value(self, x):
+        return self.critic(x)
+
+    def get_action_and_value(self, x, action=None):
+        logits = self.actor(x)
+        probs = Categorical(logits=logits)
+        if action is None:
+            action = probs.sample()
+        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
+
+
+# --- Training Loop Setup ---
+if __name__ == "__main__":
+    envs = gym.vector.SyncVectorEnv(
+        [lambda: gym.make(env_id) for _ in range(num_envs)]
+    )
+    agent = Agent(envs).to(device)
+    optimizer = optim.Adam(agent.parameters(), lr=learning_rate, eps=1e-5)
+
+    # Storage setup
+    obs = torch.zeros(
+        (num_steps, num_envs) + envs.single_observation_space.shape
+    ).to(device)
+    actions = torch.zeros(
+        (num_steps, num_envs) + envs.single_action_space.shape
+    ).to(device)
+    logprobs = torch.zeros((num_steps, num_envs)).to(device)
+    rewards = torch.zeros((num_steps, num_envs)).to(device)
+    dones = torch.zeros((num_steps, num_envs)).to(device)
+    values = torch.zeros((num_steps, num_envs)).to(device)
+
+    global_step = 0
+    next_obs, _ = envs.reset()
+    next_obs = torch.Tensor(next_obs).to(device)
+    next_done = torch.zeros(num_envs).to(device)
+
+    for iteration in range(1, total_timesteps // batch_size + 1):
+        # 1. Rollout phase
+        for step in range(num_steps):
+            global_step += num_envs
+            obs[step] = next_obs
+            dones[step] = next_done
+
+            with torch.no_grad():
+                action, logprob, _, value = agent.get_action_and_value(
+                    next_obs
+                )
+                values[step] = value.flatten()
+            actions[step] = action
+            logprobs[step] = logprob
+
+            next_obs, reward, terminations, activations, infos = envs.step(
+                action.cpu().numpy()
+            )
+            next_done = np.logical_or(terminations, activations)
+            rewards[step] = torch.tensor(reward).to(device).view(-1)
+            next_obs, next_done = (
+                torch.Tensor(next_obs).to(device),
+                torch.Tensor(next_done).to(device),
+            )
+
+        # 2. Advantage Calculation (GAE can be added here, simplified for brevity)
+        # 3. PPO Update Logic (Actor and Critic Loss)
+        # ... [Policy Update Logic goes here] ...
+
+        print(f"Step: {global_step} | Training...")
+
+    # --- Save and Upload ---
+    model_name = "ppo-LunarLander-v2"
+    torch.save(agent.state_dict(), f"{model_name}.pt")
+
+    # Upload to Hub
+    # Replace 'your-username' with your actual HF username
+    repo_id = f"your-username/{model_name}"
+    push_to_hub_fast(
+        repo_id=repo_id, folder_path="./", token=os.getenv("HF_TOKEN")
+    )
diff --git a/ppo_old.py b/ppo_old.py
new file mode 100644
index 0000000000000000000000000000000000000000..521d95eecbbdee801d9b4cbea524dac9ec0364bb
--- /dev/null
+++ b/ppo_old.py
@@ -0,0 +1,790 @@
+import argparse
+import os
+import random
+import time
+from distutils.util import strtobool
+
+import gym
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.distributions.categorical import Categorical
+from torch.utils.tensorboard import SummaryWriter
+
+
+def make_env(gym_id, seed, idx, capture_video, run_name):
+    def thunk():
+        env = gym.make(gym_id)
+        env = gym.wrappers.RecordEpisodeStatistics(env)
+        if capture_video:
+            if idx == 0:
+                env = gym.wrappers.RecordVideo(
+                    env,
+                    f"videos/{run_name}",
+                    episode_trigger=lambda t: t % 1000 == 0,
+                )
+        env.seed(seed)
+        env.action_space.seed(seed)
+        env.observation_space.seed(seed)
+        return env
+
+    return thunk
+
+
+def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
+    torch.nn.init.orthogonal_(layer.weight, std)
+    torch.nn.init.constant_(layer.bias, bias_const)
+    return layer
+
+
+class Agent(nn.Module):
+    def __init__(self, envs):
+        super(Agent, self).__init__()
+        self.critic = nn.Sequential(
+            layer_init(
+                nn.Linear(
+                    np.array(envs.single_observation_space.shape).prod(), 64
+                )
+            ),
+            nn.Tanh(),
+            layer_init(nn.Linear(64, 64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64, 1), std=1.0),
+        )
+
+        self.actor = nn.Sequential(
+            layer_init(
+                nn.Linear(
+                    np.array(envs.single_observation_space.shape).prod(), 64
+                )
+            ),
+            nn.Tanh(),
+            layer_init(nn.Linear(64, 64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64, envs.single_action_space.n), std=0.01),
+        )
+
+    def get_value(self, x):
+        return self.critic(x)
+
+    def get_action_and_value(self, x, action=None):
+        logits = self.actor(x)
+        probs = Categorical(logits=logits)
+        if action is None:
+            action = probs.sample()
+        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--exp-name",
+        type=str,
+        default=os.path.basename(__file__).rstrip(".py"),
+        help="the name of this experiment",
+    )
+    parser.add_argument(
+        "--gym-id",
+        type=str,
+        default="CartPole-v1",
+        help="the id of the gym environment",
+    )
+    parser.add_argument(
+        "--learning-rate",
+        type=float,
+        default=2.5e-4,
+        help="the learning rate of the optimizer",
+    )
+    parser.add_argument(
+        "--seed", type=int, default=1, help="seed of the experiment"
+    )
+    parser.add_argument(
+        "--total-timesteps",
+        type=int,
+        default=25000,
+        help="total timesteps of the experiments",
+    )
+    parser.add_argument(
+        "--torch-deterministic",
+        type=lambda x: bool(strtobool(x)),
+        default=True,
+        nargs="?",
+        const=True,
+        help="if toggled, `torch.backeds.cudnn.deterministic=False`",
+    )
+    parser.add_argument(
+        "--cuda",
+        type=lambda x: bool(strtobool(x)),
+        default=True,
+        nargs="?",
+        const=True,
+        help="if toggled, cuda will not be enabled by default",
+    )
+    parser.add_argument(
+        "--track",
+        type=lambda x: bool(strtobool(x)),
+        default=False,
+        nargs="?",
+        const=True,
+        help="if toggled, this experiment will be tracked with Weights and Biases",
+    )
+    parser.add_argument(
+        "--wandb-project-name",
+        type=str,
+        default="cleanRL",
+        help="the wandb's project name",
+    )
+    parser.add_argument(
+        "--wandb-entity",
+        type=str,
+        default=None,
+        help="the entity (team) of wandb's project",
+    )
+    parser.add_argument(
+        "--num-envs",
+        type=int,
+        default=4,
+        help="the number of parallel game environments",
+    )
+    parser.add_argument(
+        "--capture-video",
+        type=lambda x: bool(strtobool(x)),
+        default=False,
+        nargs="?",
+        const=True,
+        help="whether to capture videos of the agent performances (check out `videos` folder)",
+    )
+    parser.add_argument(
+        "--num-steps",
+        type=int,
+        default=128,
+        help="the number of steps to run in each environment per policy rollout",
+    )
+    parser.add_argument(
+        "--anneal-lr",
+        type=lambda x: bool(strtobool(x)),
+        default=True,
+        nargs="?",
+        const=True,
+        help="Toggle learning rate annealing for policy and value networks",
+    )
+    parser.add_argument(
+        "--gae",
+        type=lambda x: bool(strtobool(x)),
+        default=True,
+        nargs="?",
+        const=True,
+        help="Use GAE for advantage computation",
+    )
+    parser.add_argument(
+        "--gamma", type=float, default=0.99, help="the discount factor gamma"
+    )
+    parser.add_argument(
+        "--gae-lambda",
+        type=float,
+        default=0.95,
+        help="the lambda for the general advantage estimation",
+    )
+    parser.add_argument(
+        "--num-minibatches",
+        type=int,
+        default=4,
+        help="the number of mini-batches",
+    )
+    parser.add_argument(
+        "--update-epochs",
+        type=int,
+        default=4,
+        help="the K epochs to update the policy",
+    )
+    parser.add_argument(
+        "--norm-adv",
+        type=lambda x: bool(strtobool(x)),
+        default=True,
+        nargs="?",
+        const=True,
+        help="Toggles advantages normalization",
+    )
+    parser.add_argument(
+        "--clip-coef",
+        type=float,
+        default=0.2,
+        help="the surrogate clipping coefficient",
+    )
+    parser.add_argument(
+        "--clip-vloss",
+        type=lambda x: bool(strtobool(x)),
+        default=True,
+        nargs="?",
+        const=True,
+        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper",
+    )
+    parser.add_argument(
+        "--ent-coef",
+        type=float,
+        default=0.01,
+        help="coefficient of the entropy",
+    )
+    parser.add_argument(
+        "--vf-coef",
+        type=float,
+        default=0.5,
+        help="coefficient of the value function",
+    )
+    parser.add_argument(
+        "--max-grad-norm",
+        type=float,
+        default=0.5,
+        help="the maximum norm for the gradient clipping",
+    )
+    parser.add_argument(
+        "--target-kl",
+        type=float,
+        default=None,
+        help="the target KL divergence threshold",
+    )
+    args = parser.parse_args()
+    args.batch_size = int(args.num_envs * args.num_steps)
+    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+    return args
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    print(args)
+    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+    if args.track:
+        import wandb
+
+        wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            sync_tensorboard=True,
+            config=vars(args),
+            name=run_name,
+            monitor_gym=True,
+            save_code=True,
+        )
+    writer = SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s"
+        % (
+            "\n".join(
+                [f"|{key}|{value}|" for key, value in vars(args).items()]
+            )
+        ),
+    )
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device(
+        "cuda" if torch.cuda.is_available() and args.cuda else "cpu"
+    )
+
+    envs = gym.vector.SyncVectorEnv(
+        [
+            make_env(
+                args.gym_id, args.seed + i, i, args.capture_video, run_name
+            )
+            for i in range(args.num_envs)
+        ]
+    )
+    assert isinstance(envs.single_action_space, gym.spaces.Discrete), (
+        "only discrete action space is supported"
+    )
+
+    agent = Agent(envs).to(device)
+    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
+
+    # ALGO Logic: Storage setup
+    obs = torch.zeros(
+        (args.num_steps, args.num_envs) + envs.single_observation_space.shape
+    ).to(device)
+    actions = torch.zeros(
+        (args.num_steps, args.num_envs) + envs.single_action_space.shape
+    ).to(device)
+    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
+
+    # TRY NOT TO MODIFY: start the game
+    global_step = 0
+    start_time = time.time()
+    next_obs = torch.Tensor(envs.reset()).to(device)
+    next_done = torch.zeros(args.num_envs).to(device)
+    num_updates = args.total_timesteps // args.batch_size
+
+    for update in range(1, num_updates + 1):
+        # Annealing the rate if instructed to do so.abs
+        if args.anneal_lr:
+            frac = 1.0 - (update - 1.0) / num_updates
+            lrnow = frac * args.learning_rate
+            optimizer.param_groups[0]["lr"] = lrnow
+
+        for step in range(args.num_steps):
+            global_step += 1 * args.num_envs
+            obs[step] = next_obs
+            dones[step] = next_done
+
+            # ALGO LOGIC : action logic
+            with torch.no_grad():
+                action, logprob, _, value = agent.get_action_and_value(
+                    next_obs
+                )
+                values[step] = value.flatten()
+            actions[step] = action
+            logprobs[step] = logprob
+
+            # TRY NOT TO MODIFY: execute the game and log data.abs
+            next_obs, reward, done, info = envs.step(action.cpu().numpy())
+            rewards[step] = torch.tensor(reward).to(device).view(-1)
+            next_obs, next_done = (
+                torch.Tensor(next_obs).to(device),
+                torch.Tensor(done).to(device),
+            )
+
+            if isinstance(info, dict) and "episode" in info:
+                for item in info["episode"]:
+                    if item is not None:
+                        print(
+                            f"global_step={global_step}, episodic_return={item['r']}"
+                        )
+                        writer.add_scalar(
+                            "charts/episodic_return", item["r"], global_step
+                        )
+                        writer.add_scalar(
+                            "charts/episodic_length", item["l"], global_step
+                        )
+                        break
+
+            # bootstrap reward if not done
+            with torch.no_grad():
+                next_value = agent.get_value(next_obs).reshape(1, -1)
+                if args.gae:
+                    advantages = torch.zeros_like(rewards).to(device)
+                    lastgaelam = 0
+                    for t in reversed(range(args.num_steps)):
+                        if t == args.num_steps - 1:
+                            nextnonterminal = 1.0 - next_done
+                            nextvalues = next_value
+                        else:
+                            nextnonterminal = 1.0 - dones[t + 1]
+                            nextvalues = values[t + 1]
+                        delta = (
+                            rewards[t]
+                            + args.gamma * nextvalues * nextnonterminal
+                            - values[t]
+                        )
+                        advantages[t] = lastgaelam = (
+                            delta
+                            + args.gamma
+                            * args.gae_lambda
+                            * nextnonterminal
+                            * lastgaelam
+                        )
+                    returns = advantages + values
+                else:
+                    returns = torch.zeros_like(rewards).to(device)
+                    for t in reversed(range(args.num_steps)):
+                        if t == args.num_steps - 1:
+                            nextnonterminal = 1.0 - next_done
+                            next_return = next_value
+                        else:
+                            nextnonterminal = 1.0 - dones[t + 1]
+                            next_return = returns[t + 1]
+                        returns[t] = (
+                            rewards[t]
+                            + args.gamma * nextnonterminal * next_return
+                        )
+                    advantages = returns - values
+
+            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
+            b_logprobs = logprobs.reshape(-1)
+            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
+            b_advantages = advantages.reshape(-1)
+            b_returns = returns.reshape(-1)
+            b_values = values.reshape(-1)
+
+            # Optimizaing the policy and value network
+            b_inds = np.arange(args.batch_size)
+            clipfracs = []
+            for epoch in range(args.update_epochs):
+                np.random.shuffle(b_inds)
+                for start in range(0, args.batch_size, args.minibatch_size):
+                    end = start + args.minibatch_size
+                    mb_inds = b_inds[start:end]
+
+                    _, newlogprob, entropy, newvalue = (
+                        agent.get_action_and_value(  # POSIBLE ERROR AQUI
+                            b_obs[mb_inds], b_actions.long()[mb_inds]
+                        )
+                    )
+                    logratio = newlogprob - b_logprobs[mb_inds]
+                    ratio = logratio.exp()
+
+                    with torch.no_grad():
+                        # calculate approx kl as in http://joschu.net/blog/kl-aprox.html
+                        old_approx_kl = (-logratio).mean()
+                        approx_kl = ((ratio - 1) - logratio).mean()
+                        clipfracs += [
+                            ((ratio - 1.0).abs() > args.clip_coef)
+                            .float()
+                            .mean()
+                        ]
+
+                    mb_advantages = b_advantages[mb_inds]
+                    if args.norm_adv:
+                        mb_advantages = (
+                            mb_advantages - mb_advantages.mean()
+                        ) / (mb_advantages.std() + 1e-8)
+
+                    # Policy loss
+                    pg_loss1 = -mb_advantages * ratio
+                    pg_loss2 = -mb_advantages * torch.clamp(
+                        ratio, 1 - args.clip_coef, 1 + args.clip_coef
+                    )
+                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
+
+                    # Value loss
+                    newvalue = newvalue.view(-1)
+                    if args.clip_vloss:
+                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
+                        v_clipped = b_values[mb_inds] + torch.clamp(
+                            newvalue - b_values[mb_inds],
+                            -args.clip_coef,
+                            args.clip_coef,
+                        )
+                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
+                        v_loss_max = torch.max(
+                            v_loss_unclipped, v_loss_clipped
+                        )
+                        v_loss = 0.5 * v_loss_max.mean()
+                    else:
+                        v_loss = (
+                            0.5 * ((newvalue - b_returns[mb_inds]) ** 2).mean()
+                        )
+
+                    # Entropy loss
+                    entropy_loss = entropy.mean()
+                    loss = (
+                        pg_loss
+                        - args.ent_coef * entropy_loss
+                        + v_loss * args.vf_coef
+                    )
+
+                    optimizer.zero_grad()
+                    loss.backward()
+                    nn.utils.clip_grad_norm_(
+                        agent.parameters(), args.max_grad_norm
+                    )
+                    optimizer.step()
+
+                if args.target_kl is not None:
+                    if approx_kl > args.target_kl:
+                        break
+
+        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
+        var_y = np.var(y_true)
+        explained_var = (
+            np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
+        )
+
+        # TRY NOT TO MODIFY: record rewards for plotting purposes
+        writer.add_scalar(
+            "charts/learning_rate",
+            optimizer.param_groups[0]["lr"],
+            global_step,
+        )
+        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
+        writer.add_scalar("losses/policy_loss", pg_loss.item(), global_step)
+        writer.add_scalar("losses/entropy", entropy_loss.item(), global_step)
+        writer.add_scalar("losses/approx_kl", approx_kl.item(), global_step)
+        writer.add_scalar("losses/clipfrac", np.mean(clipfracs), global_step)
+        writer.add_scalar(
+            "losses/explained_variance", explained_var, global_step
+        )
+        print("SPS:", int(global_step / (time.time() - start_time)))
+        writer.add_scalar(
+            "charts/SPS",
+            int(global_step / (time.time() - start_time)),
+            global_step,
+        )
+
+    envs.close()
+    writer.close()
+
+
+##############################################################################
+############################## Huggingface ###################################
+##############################################################################
+import datetime
+import json
+import shutil
+import tempfile
+from pathlib import Path
+
+import imageio
+from huggingface_hub import HfApi, upload_folder
+from huggingface_hub.repocard import metadata_eval_result, metadata_save
+from wasabi import Printer
+
+msg = Printer()
+
+
+def package_to_hub(
+    repo_id,
+    model,
+    hyperparameters,
+    eval_env,
+    video_fps=30,
+    commit_message="Push agent to the Hub",
+    token=None,
+    logs=None,
+):
+    """
+    Evaluate, Generate a video and Upload a model to Hugging Face Hub.
+    This method does the complete pipeline:
+    - It evaluates the model
+    - It generates the model card
+    - It generates a replay video of the agent
+    - It pushes everything to the hub
+    :param repo_id: id of the model repository from the Hugging Face Hub
+    :param model: trained model
+    :param eval_env: environment used to evaluate the agent
+    :param fps: number of fps for rendering the video
+    :param commit_message: commit message
+    :param logs: directory on local machine of tensorboard logs you'd like to upload
+    """
+    msg.info(
+        "This function will save, evaluate, generate a video of your agent, "
+        "create a model card and push everything to the hub. "
+        "It might take up to 1min. \n "
+        "This is a work in progress: if you encounter a bug, please open an issue."
+    )
+    # Step 1: Clone or create the repo
+    repo_url = HfApi().create_repo(
+        repo_id=repo_id,
+        token=token,
+        private=False,
+        exist_ok=True,
+    )
+
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        tmpdirname = Path(tmpdirname)
+
+        # Step 2: Save the model
+        torch.save(model.state_dict(), tmpdirname / "model.pt")
+
+        # Step 3: Evaluate the model and build JSON
+        mean_reward, std_reward = _evaluate_agent(eval_env, 10, model)
+
+        # First get datetime
+        eval_datetime = datetime.datetime.now()
+        eval_form_datetime = eval_datetime.isoformat()
+
+        evaluate_data = {
+            "env_id": hyperparameters.env_id,
+            "mean_reward": mean_reward,
+            "std_reward": std_reward,
+            "n_evaluation_episodes": 10,
+            "eval_datetime": eval_form_datetime,
+        }
+
+        # Write a JSON file
+        with open(tmpdirname / "results.json", "w") as outfile:
+            json.dump(evaluate_data, outfile)
+
+        # Step 4: Generate a video
+        video_path = tmpdirname / "replay.mp4"
+        record_video(eval_env, model, video_path, video_fps)
+
+        # Step 5: Generate the model card
+        generated_model_card, metadata = _generate_model_card(
+            "PPO",
+            hyperparameters.env_id,
+            mean_reward,
+            std_reward,
+            hyperparameters,
+        )
+        _save_model_card(tmpdirname, generated_model_card, metadata)
+
+        # Step 6: Add logs if needed
+        if logs:
+            _add_logdir(tmpdirname, Path(logs))
+
+        msg.info(f"Pushing repo {repo_id} to the Hugging Face Hub")
+
+        repo_url = upload_folder(
+            repo_id=repo_id,
+            folder_path=tmpdirname,
+            path_in_repo="",
+            commit_message=commit_message,
+            token=token,
+        )
+
+        msg.info(
+            f"Your model is pushed to the Hub. You can view your model here: {repo_url}"
+        )
+    return repo_url
+
+
+def _evaluate_agent(env, n_eval_episodes, policy):
+    """
+    Evaluate the agent for ``n_eval_episodes`` episodes and returns average reward and std of reward.
+    :param env: The evaluation environment
+    :param n_eval_episodes: Number of episode to evaluate the agent
+    :param policy: The agent
+    """
+    episode_rewards = []
+    for episode in range(n_eval_episodes):
+        state = env.reset()
+        step = 0
+        done = False
+        total_rewards_ep = 0
+
+        while done is False:
+            state = torch.Tensor(state).to(device)
+            action, _, _, _ = policy.get_action_and_value(state)
+            new_state, reward, done, info = env.step(action.cpu().numpy())
+            total_rewards_ep += reward
+            if done:
+                break
+            state = new_state
+        episode_rewards.append(total_rewards_ep)
+    mean_reward = np.mean(episode_rewards)
+    std_reward = np.std(episode_rewards)
+
+    return mean_reward, std_reward
+
+
+def record_video(env, policy, out_directory, fps=30):
+    images = []
+    done = False
+    state = env.reset()
+    img = env.render(mode="rgb_array")
+    images.append(img)
+    while not done:
+        state = torch.Tensor(state).to(device)
+        # Take the action (index) that have the maximum expected future reward given that state
+        action, _, _, _ = policy.get_action_and_value(state)
+        state, reward, done, info = env.step(
+            action.cpu().numpy()
+        )  # We directly put next_state = state for recording logic
+        img = env.render(mode="rgb_array")
+        images.append(img)
+    imageio.mimsave(
+        out_directory, [np.array(img) for i, img in enumerate(images)], fps=fps
+    )
+
+
+def _generate_model_card(
+    model_name, env_id, mean_reward, std_reward, hyperparameters
+):
+    """
+    Generate the model card for the Hub
+    :param model_name: name of the model
+    :env_id: name of the environment
+    :mean_reward: mean reward of the agent
+    :std_reward: standard deviation of the mean reward of the agent
+    :hyperparameters: training arguments
+    """
+    # Step 1: Select the tags
+    metadata = generate_metadata(model_name, env_id, mean_reward, std_reward)
+
+    # Transform the hyperparams namespace to string
+    converted_dict = vars(hyperparameters)
+    converted_str = str(converted_dict)
+    converted_str = converted_str.split(", ")
+    converted_str = "\n".join(converted_str)
+
+    # Step 2: Generate the model card
+    model_card = f"""
+  # PPO Agent Playing {env_id}
+
+  This is a trained model of a PPO agent playing {env_id}.
+    
+  # Hyperparameters
+  ```python
+  {converted_str}
+  ```
+  """
+    return model_card, metadata
+
+
+def generate_metadata(model_name, env_id, mean_reward, std_reward):
+    """
+    Define the tags for the model card
+    :param model_name: name of the model
+    :param env_id: name of the environment
+    :mean_reward: mean reward of the agent
+    :std_reward: standard deviation of the mean reward of the agent
+    """
+    metadata = {}
+    metadata["tags"] = [
+        env_id,
+        "ppo",
+        "deep-reinforcement-learning",
+        "reinforcement-learning",
+        "custom-implementation",
+        "deep-rl-course",
+    ]
+
+    # Add metrics
+    eval = metadata_eval_result(
+        model_pretty_name=model_name,
+        task_pretty_name="reinforcement-learning",
+        task_id="reinforcement-learning",
+        metrics_pretty_name="mean_reward",
+        metrics_id="mean_reward",
+        metrics_value=f"{mean_reward:.2f} +/- {std_reward:.2f}",
+        dataset_pretty_name=env_id,
+        dataset_id=env_id,
+    )
+
+    # Merges both dictionaries
+    metadata = {**metadata, **eval}
+
+    return metadata
+
+
+def _save_model_card(local_path, generated_model_card, metadata):
+    """Saves a model card for the repository.
+    :param local_path: repository directory
+    :param generated_model_card: model card generated by _generate_model_card()
+    :param metadata: metadata
+    """
+    readme_path = local_path / "README.md"
+    readme = ""
+    if readme_path.exists():
+        with readme_path.open("r", encoding="utf8") as f:
+            readme = f.read()
+    else:
+        readme = generated_model_card
+
+    with readme_path.open("w", encoding="utf-8") as f:
+        f.write(readme)
+
+    # Save our metrics to Readme metadata
+    metadata_save(readme_path, metadata)
+
+
+def _add_logdir(local_path: Path, logdir: Path):
+    """Adds a logdir to the repository.
+    :param local_path: repository directory
+    :param logdir: logdir directory
+    """
+    if logdir.exists() and logdir.is_dir():
+        # Add the logdir to the repository under new dir called logs
+        repo_logdir = local_path / "logs"
+
+        # Delete current logs if they exist
+        if repo_logdir.exists():
+            shutil.rmtree(repo_logdir)
+
+        # Copy logdir into repo logdir
+        shutil.copytree(logdir, repo_logdir)
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..d9caf9cd416696736772fc1f649b95d175eaf537
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,23 @@
+[tool.poetry]
+name = "lunar2"
+version = "0.1.0"
+description = "Training a model on Python 3.10 for HF Hub"
+authors = ["Lois Fernandez <loisfernandezcmp@gmail.com>"]
+
+[tool.poetry.dependencies]
+# Restricting to Python 3.10 specifically
+python = "~3.10"
+
+# Core ML stack
+torch = "^2.0.0"
+transformers = {extras = ["torch"], version = "^4.35.0"}
+datasets = "^2.15.0"
+accelerate = "^0.24.0"
+
+# For uploading to the Hugging Face Hub
+huggingface-hub = "^0.19.0"
+gymnasium = {version = "0.28.1", extras = ["box2d"]}
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"
\ No newline at end of file
diff --git a/ruff.toml b/ruff.toml
new file mode 100644
index 0000000000000000000000000000000000000000..11eb23fedc2bcb128d7468bdf82323c3b32d39a2
--- /dev/null
+++ b/ruff.toml
@@ -0,0 +1,104 @@
+# Exclude a variety of commonly ignored directories.
+exclude = [
+    ".bzr",
+    ".direnv",
+    ".eggs",
+    ".git",
+    ".git-rewrite",
+    ".hg",
+    ".ipynb_checkpoints",
+    ".mypy_cache",
+    ".nox",
+    ".pants.d",
+    ".pyenv",
+    ".pytest_cache",
+    ".pytype",
+    ".ruff_cache",
+    ".svn",
+    ".tox",
+    ".venv",
+    ".vscode",
+    "__pypackages__",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "node_modules",
+    "site-packages",
+    "venv",
+    "*/test/*",
+    "*/tests/*",
+    "*/tools/*",
+]
+
+# Same as Black.
+line-length = 79
+indent-width = 4
+
+# Assume Python 3.10
+target-version = "py310"
+
+[lint]
+# Enable these specific rules and rule-subsets.
+select = [
+  "E4", 
+  "E7", 
+  "E9", 
+  "F", 
+  "ERA", 
+  "FAST", 
+  "ANN", 
+  "ASYNC", 
+  "S303", 
+  "S304", 
+  "FBT", 
+  "B", 
+  "A", 
+  "COM818", 
+  "C4", 
+  "FA", 
+  "ICN", 
+  "PIE", 
+  "Q", 
+  "RET", 
+  "SIM", 
+  "ARG", 
+  "PTH", 
+  "FLY", 
+  "C90", 
+  "NPY", 
+  "PD", 
+  "PLE03",
+  "D101",
+  "D102",
+  "D103",
+  "D105",
+  "D107",
+  "D201",
+  "D403",
+  "D419"
+]
+ignore = ["ANN101", "ANN102","ANN002", "ANN003", "ANN401", "PIE790", "RET504"]
+
+# Allow fix for all enabled rules (when `--fix`) is provided.
+fixable = ["ALL"]
+unfixable = []
+
+# Allow unused variables when underscore-prefixed.
+dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
+
+[lint.per-file-ignores]
+"__init__.py" = ["F401"]
+
+[format]
+# Like Black, use double quotes for strings.
+quote-style = "double"
+
+# Like Black, indent with spaces, rather than tabs.
+indent-style = "space"
+
+# Like Black, respect magic trailing commas.
+skip-magic-trailing-comma = false
+
+# Like Black, automatically detect the appropriate line ending.
+line-ending = "auto"
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diff --git a/runs/CartPole-v1_ppo_1_1776760089/events.out.tfevents.1776760089.BL-LFERNANDEZ.local.eurecat.org.17273.0 b/runs/CartPole-v1_ppo_1_1776760089/events.out.tfevents.1776760089.BL-LFERNANDEZ.local.eurecat.org.17273.0
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@@ -0,0 +1,3 @@
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diff --git a/tea_debug.log b/tea_debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/upload.py b/upload.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf8c9095376bbe85f20832815e2cbd951702577c
--- /dev/null
+++ b/upload.py
@@ -0,0 +1,16 @@
+from huggingface_hub import HfApi
+
+# Assuming you saved your model locally during training
+model_path = "./"
+repo_id = "loisonchambers/lunar2"
+
+# 1. Initialize API
+api = HfApi()
+
+# 2. Create the repo (if it doesn't exist)
+api.create_repo(repo_id=repo_id, exist_ok=True)
+
+# 3. Upload your folder
+api.upload_folder(folder_path=model_path, repo_id=repo_id, repo_type="model")
+
+print(f"Model successfully uploaded to: https://huggingface.co/{repo_id}")
diff --git a/videos/CartPole-v1_ppo_1_1776780080/rl-video-episode-0.meta.json b/videos/CartPole-v1_ppo_1_1776780080/rl-video-episode-0.meta.json
new file mode 100644
index 0000000000000000000000000000000000000000..4cdd306b01be38c98853b88d33ad23e09cc81f4f
--- /dev/null
+++ b/videos/CartPole-v1_ppo_1_1776780080/rl-video-episode-0.meta.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/videos/CartPole-v1_ppo_1_1776780080/rl-video-episode-0.mp4 b/videos/CartPole-v1_ppo_1_1776780080/rl-video-episode-0.mp4
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index 0000000000000000000000000000000000000000..1954a9b0efb09889c8cd5ad8128e601548e56441
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diff --git a/videos/CartPole-v1_ppo_1_1776783356/rl-video-episode-0.meta.json b/videos/CartPole-v1_ppo_1_1776783356/rl-video-episode-0.meta.json
new file mode 100644
index 0000000000000000000000000000000000000000..c5180f9ffae8eb302095e65973aada767607b235
--- /dev/null
+++ b/videos/CartPole-v1_ppo_1_1776783356/rl-video-episode-0.meta.json
@@ -0,0 +1 @@
+{"step_id": 0, "episode_id": 0, "content_type": "video/mp4", "encoder_version": {"backend": "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/imageio_ffmpeg/binaries/ffmpeg-linux-x86_64-v7.0.2", "version": "b'ffmpeg version 7.0.2-static https://johnvansickle.com/ffmpeg/  Copyright (c) 2000-2024 the FFmpeg developers\\nbuilt with gcc 8 (Debian 8.3.0-6)\\nconfiguration: --enable-gpl --enable-version3 --enable-static --disable-debug --disable-ffplay --disable-indev=sndio --disable-outdev=sndio --cc=gcc --enable-fontconfig --enable-frei0r --enable-gnutls --enable-gmp --enable-libgme --enable-gray --enable-libaom --enable-libfribidi --enable-libass --enable-libvmaf --enable-libfreetype --enable-libmp3lame --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-librubberband --enable-libsoxr --enable-libspeex --enable-libsrt --enable-libvorbis --enable-libopus --enable-libtheora --enable-libvidstab --enable-libvo-amrwbenc --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libdav1d --enable-libxvid --enable-libzvbi --enable-libzimg\\nlibavutil      59.  8.100 / 59.  8.100\\nlibavcodec     61.  3.100 / 61.  3.100\\nlibavformat    61.  1.100 / 61.  1.100\\nlibavdevice    61.  1.100 / 61.  1.100\\nlibavfilter    10.  1.100 / 10.  1.100\\nlibswscale      8.  1.100 /  8.  1.100\\nlibswresample   5.  1.100 /  5.  1.100\\nlibpostproc    58.  1.100 / 58.  1.100\\n'", "cmdline": ["/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/imageio_ffmpeg/binaries/ffmpeg-linux-x86_64-v7.0.2", "-nostats", "-loglevel", "error", "-y", "-f", "rawvideo", "-s:v", "600x400", "-pix_fmt", "rgb24", "-framerate", "50", "-i", "-", "-vf", "scale=trunc(iw/2)*2:trunc(ih/2)*2", "-vcodec", "libx264", "-pix_fmt", "yuv420p", "-r", "50", "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/videos/CartPole-v1_ppo_1_1776783356/rl-video-episode-0.mp4"]}}
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diff --git a/videos/CartPole-v1_ppo_1_1776783356/rl-video-episode-0.mp4 b/videos/CartPole-v1_ppo_1_1776783356/rl-video-episode-0.mp4
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diff --git a/videos/rl-video-episode-0.meta.json b/videos/rl-video-episode-0.meta.json
new file mode 100644
index 0000000000000000000000000000000000000000..b784bf2865074739d0137df0835c601994e210ad
--- /dev/null
+++ b/videos/rl-video-episode-0.meta.json
@@ -0,0 +1 @@
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new file mode 100644
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--- /dev/null
+++ b/wandb/debug-internal.log
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+{"time":"2026-04-21T16:55:58.155280809+02:00","level":"INFO","msg":"wandb-core"}
+{"time":"2026-04-21T16:55:58.157097837+02:00","level":"INFO","msg":"stream: starting","core version":"0.26.0"}
+{"time":"2026-04-21T16:55:58.828210699+02:00","level":"INFO","msg":"stream: created new stream","id":"ngbvlof1"}
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+{"time":"2026-04-21T16:56:09.370874655+02:00","level":"WARN","msg":"tensorboard: no root directory","error":"timed out after 10s"}
+{"time":"2026-04-21T16:56:09.371058525+02:00","level":"INFO","msg":"tensorboard: tracking new log directory","rootDir":{},"logDir":"LocalOrCloudPath(LocalPath=\"/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/runs/CartPole-v1_ppo_1_1776783356\")","namespace":""}
+{"time":"2026-04-21T16:56:09.372710786+02:00","level":"INFO","msg":"tensorboard: saving file","fileLocation":"/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/runs/CartPole-v1_ppo_1_1776783356/events.out.tfevents.1776783359.BL-LFERNANDEZ.local.eurecat.org.1005872.0","runPath":"runs/CartPole-v1_ppo_1_1776783356/events.out.tfevents.1776783359.BL-LFERNANDEZ.local.eurecat.org.1005872.0"}
+{"time":"2026-04-21T16:56:14.385129817+02:00","level":"INFO","msg":"filestream: sending request","total_files":4,"history_offset":0,"history_lines":16,"events_offset":0,"events_lines":1,"console_offset":0,"console_lines":26}
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+{"time":"2026-04-21T17:10:42.916160051+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": net/http: request canceled (Client.Timeout exceeded while awaiting headers)"}
+{"time":"2026-04-21T17:10:44.481695952+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": context deadline exceeded"}
+{"time":"2026-04-21T17:10:51.54442006+02:00","level":"INFO","msg":"filestream: sending request","total_files":0}
+{"time":"2026-04-21T17:10:51.838172161+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
+{"time":"2026-04-21T17:10:53.063732084+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": net/http: request canceled (Client.Timeout exceeded while awaiting headers)"}
+{"time":"2026-04-21T17:11:01.443767432+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": context deadline exceeded"}
+{"time":"2026-04-21T17:11:21.545051979+02:00","level":"INFO","msg":"filestream: sending request","total_files":0}
+{"time":"2026-04-21T17:11:21.841717689+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
+{"time":"2026-04-21T17:11:51.546125246+02:00","level":"INFO","msg":"filestream: sending request","total_files":0}
+{"time":"2026-04-21T17:11:51.946730405+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
+{"time":"2026-04-21T17:12:10.337929259+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": context deadline exceeded"}
+{"time":"2026-04-21T17:12:12.917385036+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": net/http: request canceled (Client.Timeout exceeded while awaiting headers)"}
+{"time":"2026-04-21T17:12:14.4835765+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": net/http: request canceled (Client.Timeout exceeded while awaiting headers)"}
+{"time":"2026-04-21T17:12:21.547011677+02:00","level":"INFO","msg":"filestream: sending request","total_files":0}
+{"time":"2026-04-21T17:12:21.848063887+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
+{"time":"2026-04-21T17:12:23.065210244+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": context deadline exceeded"}
+{"time":"2026-04-21T17:12:51.547457202+02:00","level":"INFO","msg":"filestream: sending request","total_files":0}
+{"time":"2026-04-21T17:12:51.852643569+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
+{"time":"2026-04-21T17:13:21.547809349+02:00","level":"INFO","msg":"filestream: sending request","total_files":0}
+{"time":"2026-04-21T17:13:21.855333325+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
+{"time":"2026-04-21T17:13:26.310857254+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": read tcp 172.20.140.252:53572->34.8.250.101:443: read: no route to host"}
+{"time":"2026-04-21T17:13:26.310907952+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": read tcp 172.20.140.252:53572->34.8.250.101:443: read: no route to host"}
+{"time":"2026-04-21T17:13:26.310938354+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": read tcp 172.20.140.252:53572->34.8.250.101:443: read: no route to host"}
+{"time":"2026-04-21T17:13:26.310869305+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": read tcp 172.20.140.252:53572->34.8.250.101:443: read: no route to host"}
+{"time":"2026-04-21T17:13:51.548429786+02:00","level":"INFO","msg":"filestream: sending request","total_files":0}
+{"time":"2026-04-21T17:13:51.76499276+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
+{"time":"2026-04-21T17:14:21.548997565+02:00","level":"INFO","msg":"filestream: sending request","total_files":0}
+{"time":"2026-04-21T17:14:21.863485527+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
+{"time":"2026-04-21T17:14:26.954473618+02:00","level":"INFO","msg":"filestream: sending request","total_files":0,"uploaded_len":1}
+{"time":"2026-04-21T17:14:27.168039023+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
+{"time":"2026-04-21T17:14:27.795661028+02:00","level":"INFO","msg":"fileTransfer: Close: file transfer manager closed"}
+{"time":"2026-04-21T17:14:27.795748862+02:00","level":"INFO","msg":"filestream: sending request","total_files":0,"uploaded_len":8,"complete":true,"exit_code":0}
+{"time":"2026-04-21T17:14:28.04832358+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
+{"time":"2026-04-21T17:14:28.048473825+02:00","level":"INFO","msg":"handler: closed"}
+{"time":"2026-04-21T17:14:28.048576801+02:00","level":"INFO","msg":"sender: closed"}
+{"time":"2026-04-21T17:14:28.048583706+02:00","level":"INFO","msg":"stream: closed"}
diff --git a/wandb/debug.log b/wandb/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..b0923e4766535294adfb22b97e9ca7adf5b7cbad
--- /dev/null
+++ b/wandb/debug.log
@@ -0,0 +1,22 @@
+2026-04-21 16:55:57,786 INFO    MainThread:1005872 [wandb_setup.py:_flush():81] Current SDK version is 0.26.0
+2026-04-21 16:55:57,787 INFO    MainThread:1005872 [wandb_setup.py:_flush():81] Configure stats pid to 1005872
+2026-04-21 16:55:57,787 INFO    MainThread:1005872 [wandb_setup.py:_flush():81] Loading settings from environment variables
+2026-04-21 16:55:57,787 INFO    MainThread:1005872 [wandb_init.py:setup_run_log_directory():721] Logging user logs to /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/wandb/run-20260421_165557-ngbvlof1/logs/debug.log
+2026-04-21 16:55:57,787 INFO    MainThread:1005872 [wandb_init.py:setup_run_log_directory():722] Logging internal logs to /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/wandb/run-20260421_165557-ngbvlof1/logs/debug-internal.log
+2026-04-21 16:55:57,843 INFO    MainThread:1005872 [wandb_init.py:init():848] calling init triggers
+2026-04-21 16:55:57,843 INFO    MainThread:1005872 [wandb_init.py:init():853] wandb.init called with sweep_config: {}
+config: {'exp_name': 'ppo', 'gym_id': 'CartPole-v1', 'learning_rate': 0.00025, 'seed': 1, 'total_timesteps': 25000, 'torch_deterministic': True, 'cuda': True, 'track': True, 'wandb_project_name': 'cleanRL', 'wandb_entity': None, 'num_envs': 4, 'capture_video': True, 'num_steps': 128, 'anneal_lr': True, 'gae': True, 'gamma': 0.99, 'gae_lambda': 0.95, 'num_minibatches': 4, 'update_epochs': 4, 'norm_adv': True, 'clip_coef': 0.2, 'clip_vloss': True, 'ent_coef': 0.01, 'vf_coef': 0.5, 'max_grad_norm': 0.5, 'target_kl': None, 'batch_size': 512, 'minibatch_size': 128, '_wandb': {'code_path': 'code/ppo.py'}}
+2026-04-21 16:55:57,843 INFO    MainThread:1005872 [wandb_init.py:init():896] starting backend
+2026-04-21 16:55:58,111 INFO    MainThread:1005872 [wandb_init.py:init():911] sending inform_init request
+2026-04-21 16:55:58,149 INFO    MainThread:1005872 [wandb_init.py:init():919] backend started and connected
+2026-04-21 16:55:58,183 INFO    MainThread:1005872 [wandb_init.py:init():989] updated telemetry
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+2026-04-21 16:55:59,249 INFO    MainThread:1005872 [wandb_init.py:init():1058] starting run threads in backend
+2026-04-21 16:55:59,360 INFO    MainThread:1005872 [wandb_run.py:_console_start():2542] atexit reg
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+2026-04-21 16:55:59,360 INFO    MainThread:1005872 [wandb_run.py:_redirect():2460] Wrapping output streams.
+2026-04-21 16:55:59,360 INFO    MainThread:1005872 [wandb_run.py:_redirect():2483] Redirects installed.
+2026-04-21 16:55:59,367 INFO    MainThread:1005872 [wandb_init.py:init():1098] run started, returning control to user process
+2026-04-21 16:55:59,368 INFO    MainThread:1005872 [wandb_run.py:_tensorboard_callback():1632] tensorboard callback: runs/CartPole-v1_ppo_1_1776783356, True
+2026-04-21 17:07:51,536 INFO    wandb-AsyncioManager-main:1005872 [service_client.py:_forward_responses():134] Reached EOF.
+2026-04-21 17:07:51,537 INFO    wandb-AsyncioManager-main:1005872 [mailbox.py:close():155] Closing mailbox, abandoning 1 handles.
diff --git a/wandb/run-20260421_103958-if0q7puu/files/code/ppo.py b/wandb/run-20260421_103958-if0q7puu/files/code/ppo.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1f631ae66b6d80eea313e9326b7696da4ff2769
--- /dev/null
+++ b/wandb/run-20260421_103958-if0q7puu/files/code/ppo.py
@@ -0,0 +1,54 @@
+import argparse
+import os
+from distutils.util import strtobool
+import time
+from torch.utils.tensorboard import SummaryWriter
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+        help='the name of this experiment')
+    parser.add_argument('--gym-id', type=str, default="CartPole-v1",
+        help='the id of the gym environment')
+    parser.add_argument('--learning-rate', type=float, default=2.5e-4,
+        help='the learning rate of the optimizer')
+    parser.add_argument('--seed', type=int, default=1,
+        help='seed of the experiment')
+    parser.add_argument('--total-timesteps', type=int, default=25000,
+        help='total timesteps of the experiments')
+    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+    parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, cuda will not be enabled by default')
+    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
+        help='if toggled, this experiment will be tracked with Weights and Biases')
+    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
+        help="the wandb's project name")
+    parser.add_argument('--wandb-entity', type=str, default=None,
+        help="the entity (team) of wandb's project")
+    args = parser.parse_args()
+    return args
+
+if __name__ == "__main__":
+    args = parse_args()
+    print(args)
+    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+    if args.track:
+        import wandb
+
+        wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            sync_tensorboard=True,
+            config=vars(args),
+            name=run_name,
+            monitor_gym=True,
+            save_code=True,
+        )
+    writer =SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
+    )
+    for i in range(100):
+        writer.add_scalar("test_loss", i*2, global_step=i)
\ No newline at end of file
diff --git a/wandb/run-20260421_103958-if0q7puu/files/config.yaml b/wandb/run-20260421_103958-if0q7puu/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8115d69aeff5c11102f9fb492fe5c532de807018
--- /dev/null
+++ b/wandb/run-20260421_103958-if0q7puu/files/config.yaml
@@ -0,0 +1,66 @@
+_wandb:
+    value:
+        cli_version: 0.26.0
+        code_path: code/ppo.py
+        e:
+            c19uwkemcec75pa3bb2fpxuplfr1n5ol:
+                args:
+                    - --track
+                codePath: ppo.py
+                codePathLocal: ppo.py
+                cpu_count: 12
+                cpu_count_logical: 14
+                disk:
+                    /:
+                        total: "499680116736"
+                        used: "139940552704"
+                email: loisfernandezcmp@gmail.com
+                executable: /home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/bin/python
+                git:
+                    commit: e1dc52c8934ce180fef18fbdf53a72281f134a52
+                    remote: https://github.com/loisonchambers/Unit-8-RL-course.git
+                host: BL-LFERNANDEZ.local.eurecat.org
+                memory:
+                    total: "33076023296"
+                os: Linux-6.17.0-20-generic-x86_64-with-glibc2.39
+                program: /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py
+                python: CPython 3.9.5
+                root: /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8
+                startedAt: "2026-04-21T08:39:58.879224Z"
+                writerId: c19uwkemcec75pa3bb2fpxuplfr1n5ol
+        m: []
+        python_version: 3.9.5
+        t:
+            "1":
+                - 1
+            "2":
+                - 1
+            "3":
+                - 13
+                - 16
+                - 35
+                - 41
+            "4": 3.9.5
+            "5": 0.26.0
+            "12": 0.26.0
+            "13": linux-x86_64
+cuda:
+    value: true
+exp_name:
+    value: ppo
+gym_id:
+    value: CartPole-v1
+learning_rate:
+    value: 0.00025
+seed:
+    value: 1
+torch deterministic:
+    value: true
+total_timesteps:
+    value: 25000
+track:
+    value: true
+wandb_entity:
+    value: null
+wandb_project_name:
+    value: cleanRL
diff --git a/wandb/run-20260421_103958-if0q7puu/files/diff.patch b/wandb/run-20260421_103958-if0q7puu/files/diff.patch
new file mode 100644
index 0000000000000000000000000000000000000000..a0888cea29753fcba2624521c57a313c5c42c195
--- /dev/null
+++ b/wandb/run-20260421_103958-if0q7puu/files/diff.patch
@@ -0,0 +1,820 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..6d17525 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
++    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"},
++    {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"},
++]
++
++[[package]]
++name = "charset-normalizer"
++version = "3.4.7"
++description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "charset_normalizer-3.4.7-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:cdd68a1fb318e290a2077696b7eb7a21a49163c455979c639bf5a5dcdc46617d"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e17b8d5d6a8c47c85e68ca8379def1303fd360c3e22093a807cd34a71cd082b8"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:511ef87c8aec0783e08ac18565a16d435372bc1ac25a91e6ac7f5ef2b0bff790"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:007d05ec7321d12a40227aae9e2bc6dca73f3cb21058999a1df9e193555a9dcc"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:cf29836da5119f3c8a8a70667b0ef5fdca3bb12f80fd06487cfa575b3909b393"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux_2_31_armv7l.whl", hash = "sha256:12d8baf840cc7889b37c7c770f478adea7adce3dcb3944d02ec87508e2dcf153"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:d560742f3c0d62afaccf9f41fe485ed69bd7661a241f86a3ef0f0fb8b1a397af"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:b14b2d9dac08e28bb8046a1a0434b1750eb221c8f5b87a68f4fa11a6f97b5e34"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_armv7l.whl", hash = "sha256:bc17a677b21b3502a21f66a8cc64f5bfad4df8a0b8434d661666f8ce90ac3af1"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:750e02e074872a3fad7f233b47734166440af3cdea0add3e95163110816d6752"},
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++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:6ed74185b2db44f41ef35fd1617c5888e59792da9bbc9190d6c7300617182616"},
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++    {file = "charset_normalizer-3.4.7-cp310-cp310-win32.whl", hash = "sha256:6785f414ae0f3c733c437e0f3929197934f526d19dfaa75e18fdb4f94c6fb374"},
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++
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++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
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++
++[package.extras]
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++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
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++
++[[package]]
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++
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++
+ [[package]]
+ name = "grpcio"
+ version = "1.80.0"
+@@ -334,6 +555,20 @@ other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-pyt
+ testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
+ toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+ 
++[[package]]
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++description = "Internationalized Domain Names in Applications (IDNA)"
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++
+ [[package]]
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+ version = "8.7.1"
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+ typing = ["typing-extensions"]
+ xmp = ["defusedxml"]
+ 
++[[package]]
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++type = ["mypy (>=1.14.1)"]
++
+ [[package]]
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+@@ -953,6 +1204,159 @@ files = [
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++
++[package.dependencies]
++typing-extensions = ">=4.14.1"
++
+ [[package]]
+ name = "pytest"
+ version = "7.4.4"
+@@ -976,24 +1380,197 @@ tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
+ testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
+ 
+ [[package]]
+-name = "setuptools"
+-version = "82.0.1"
+-description = "Most extensible Python build backend with support for C/C++ extension modules"
++name = "pyyaml"
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++grpcio = ["grpcio (>=1.21.1)", "protobuf (>=3.8.0)"]
++http2 = ["httpcore[http2] (==1.*)"]
++httpx = ["httpx (>=0.16.0)"]
++huey = ["huey (>=2)"]
++huggingface-hub = ["huggingface_hub (>=0.22)"]
++langchain = ["langchain (>=0.0.210)"]
++langgraph = ["langgraph (>=0.6.6)"]
++launchdarkly = ["launchdarkly-server-sdk (>=9.8.0)"]
++litellm = ["litellm (>=1.77.5,!=1.82.7,!=1.82.8)"]
++litestar = ["litestar (>=2.0.0)"]
++loguru = ["loguru (>=0.5)"]
++mcp = ["mcp (>=1.15.0)"]
++openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
++openfeature = ["openfeature-sdk (>=0.7.1)"]
++opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
++opentelemetry-experimental = ["opentelemetry-distro"]
++opentelemetry-otlp = ["opentelemetry-distro[otlp] (>=0.35b0)"]
++pure-eval = ["asttokens", "executing", "pure_eval"]
++pydantic-ai = ["pydantic-ai (>=1.0.0)"]
++pymongo = ["pymongo (>=3.1)"]
++pyspark = ["pyspark (>=2.4.4)"]
++quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
++rq = ["rq (>=0.6)"]
++sanic = ["sanic (>=0.8)"]
++sqlalchemy = ["sqlalchemy (>=1.2)"]
++starlette = ["starlette (>=0.19.1)"]
++starlite = ["starlite (>=1.48)"]
++statsig = ["statsig (>=0.55.3)"]
++tornado = ["tornado (>=6)"]
++unleash = ["UnleashClient (>=6.0.1)"]
++
++[[package]]
++name = "setuptools"
++version = "65.5.0"
++description = "Easily download, build, install, upgrade, and uninstall Python packages"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "setuptools-65.5.0-py3-none-any.whl", hash = "sha256:f62ea9da9ed6289bfe868cd6845968a2c854d1427f8548d52cae02a42b4f0356"},
++    {file = "setuptools-65.5.0.tar.gz", hash = "sha256:512e5536220e38146176efb833d4a62aa726b7bbff82cfbc8ba9eaa3996e0b17"},
++]
++
++[package.extras]
++docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
++testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
++testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
++
++[[package]]
++name = "smmap"
++version = "5.0.3"
++description = "A pure Python implementation of a sliding window memory map manager"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "smmap-5.0.3-py3-none-any.whl", hash = "sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f"},
++    {file = "smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c"},
++]
+ 
+ [[package]]
+ name = "sympy"
+@@ -1197,6 +1774,81 @@ files = [
+     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+ ]
+ 
++[[package]]
++name = "typing-inspection"
++version = "0.4.2"
++description = "Runtime typing introspection tools"
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
++    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
++]
++
++[package.dependencies]
++typing-extensions = ">=4.12.0"
++
++[[package]]
++name = "urllib3"
++version = "2.6.3"
++description = "HTTP library with thread-safe connection pooling, file post, and more."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
++brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"]
++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:1ece94a2a5eda1d0e3a2d8a2fd28aa0187705d6efa5ac4c0b8680083583b7ec1"},
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:92f6f303fe2af50e3f711833a835150f9b4d8082874bfd9868cf15491ea2947e"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:a17aae051a31831388cff880251c1b5bc38fbf6a283a0ee7c543709e8e9633d1"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:28a14ffc014e523498d077dfde12839b7be586ca8c3190e72e7167c1aea6ee4c"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1888,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "ef8f3d3357676ca2a1abf116a35e9b4204b241f751b10f3665d652afee908aba"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..c1f631a 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -20,17 +20,35 @@ def parse_args():
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
+     args = parser.parse_args()
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    for i in range(100):
++        writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..645ea93 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,15 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_103958-if0q7puu/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch b/wandb/run-20260421_103958-if0q7puu/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
new file mode 100644
index 0000000000000000000000000000000000000000..a0888cea29753fcba2624521c57a313c5c42c195
--- /dev/null
+++ b/wandb/run-20260421_103958-if0q7puu/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
@@ -0,0 +1,820 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..6d17525 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
++    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"},
++    {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"},
++]
++
++[[package]]
++name = "charset-normalizer"
++version = "3.4.7"
++description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "charset_normalizer-3.4.7-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:cdd68a1fb318e290a2077696b7eb7a21a49163c455979c639bf5a5dcdc46617d"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e17b8d5d6a8c47c85e68ca8379def1303fd360c3e22093a807cd34a71cd082b8"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:511ef87c8aec0783e08ac18565a16d435372bc1ac25a91e6ac7f5ef2b0bff790"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:007d05ec7321d12a40227aae9e2bc6dca73f3cb21058999a1df9e193555a9dcc"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:cf29836da5119f3c8a8a70667b0ef5fdca3bb12f80fd06487cfa575b3909b393"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux_2_31_armv7l.whl", hash = "sha256:12d8baf840cc7889b37c7c770f478adea7adce3dcb3944d02ec87508e2dcf153"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:d560742f3c0d62afaccf9f41fe485ed69bd7661a241f86a3ef0f0fb8b1a397af"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:b14b2d9dac08e28bb8046a1a0434b1750eb221c8f5b87a68f4fa11a6f97b5e34"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_armv7l.whl", hash = "sha256:bc17a677b21b3502a21f66a8cc64f5bfad4df8a0b8434d661666f8ce90ac3af1"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:750e02e074872a3fad7f233b47734166440af3cdea0add3e95163110816d6752"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_riscv64.whl", hash = "sha256:4e5163c14bffd570ef2affbfdd77bba66383890797df43dc8b4cc7d6f500bf53"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:6ed74185b2db44f41ef35fd1617c5888e59792da9bbc9190d6c7300617182616"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:94e1885b270625a9a828c9793b4d52a64445299baa1fea5a173bf1d3dd9a1a5a"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-win32.whl", hash = "sha256:6785f414ae0f3c733c437e0f3929197934f526d19dfaa75e18fdb4f94c6fb374"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-win_amd64.whl", hash = "sha256:6696b7688f54f5af4462118f0bfa7c1621eeb87154f77fa04b9295ce7a8f2943"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-win_arm64.whl", hash = "sha256:66671f93accb62ed07da56613636f3641f1a12c13046ce91ffc923721f23c008"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:7641bb8895e77f921102f72833904dcd9901df5d6d72a2ab8f31d04b7e51e4e7"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:202389074300232baeb53ae2569a60901f7efadd4245cf3a3bf0617d60b439d7"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:30b8d1d8c52a48c2c5690e152c169b673487a2a58de1ec7393196753063fcd5e"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:532bc9bf33a68613fd7d65e4b1c71a6a38d7d42604ecf239c77392e9b4e8998c"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2fe249cb4651fd12605b7288b24751d8bfd46d35f12a20b1ba33dea122e690df"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-manylinux_2_31_armv7l.whl", hash = "sha256:65bcd23054beab4d166035cabbc868a09c1a49d1efe458fe8e4361215df40265"},
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++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
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++
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++
+ [[package]]
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+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
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+ tqdm = ["tqdm"]
+ 
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++
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++
+ [[package]]
+ name = "grpcio"
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+@@ -334,6 +555,20 @@ other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-pyt
+ testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
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+ 
++[[package]]
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++
+ [[package]]
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+ typing = ["typing-extensions"]
+ xmp = ["defusedxml"]
+ 
++[[package]]
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++
+ [[package]]
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++mcp = ["mcp (>=1.15.0)"]
++openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
++openfeature = ["openfeature-sdk (>=0.7.1)"]
++opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
++opentelemetry-experimental = ["opentelemetry-distro"]
++opentelemetry-otlp = ["opentelemetry-distro[otlp] (>=0.35b0)"]
++pure-eval = ["asttokens", "executing", "pure_eval"]
++pydantic-ai = ["pydantic-ai (>=1.0.0)"]
++pymongo = ["pymongo (>=3.1)"]
++pyspark = ["pyspark (>=2.4.4)"]
++quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
++rq = ["rq (>=0.6)"]
++sanic = ["sanic (>=0.8)"]
++sqlalchemy = ["sqlalchemy (>=1.2)"]
++starlette = ["starlette (>=0.19.1)"]
++starlite = ["starlite (>=1.48)"]
++statsig = ["statsig (>=0.55.3)"]
++tornado = ["tornado (>=6)"]
++unleash = ["UnleashClient (>=6.0.1)"]
++
++[[package]]
++name = "setuptools"
++version = "65.5.0"
++description = "Easily download, build, install, upgrade, and uninstall Python packages"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "setuptools-65.5.0-py3-none-any.whl", hash = "sha256:f62ea9da9ed6289bfe868cd6845968a2c854d1427f8548d52cae02a42b4f0356"},
++    {file = "setuptools-65.5.0.tar.gz", hash = "sha256:512e5536220e38146176efb833d4a62aa726b7bbff82cfbc8ba9eaa3996e0b17"},
++]
++
++[package.extras]
++docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
++testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
++testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
++
++[[package]]
++name = "smmap"
++version = "5.0.3"
++description = "A pure Python implementation of a sliding window memory map manager"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "smmap-5.0.3-py3-none-any.whl", hash = "sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f"},
++    {file = "smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c"},
++]
+ 
+ [[package]]
+ name = "sympy"
+@@ -1197,6 +1774,81 @@ files = [
+     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+ ]
+ 
++[[package]]
++name = "typing-inspection"
++version = "0.4.2"
++description = "Runtime typing introspection tools"
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
++    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
++]
++
++[package.dependencies]
++typing-extensions = ">=4.12.0"
++
++[[package]]
++name = "urllib3"
++version = "2.6.3"
++description = "HTTP library with thread-safe connection pooling, file post, and more."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
++brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"]
++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:1ece94a2a5eda1d0e3a2d8a2fd28aa0187705d6efa5ac4c0b8680083583b7ec1"},
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:92f6f303fe2af50e3f711833a835150f9b4d8082874bfd9868cf15491ea2947e"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:a17aae051a31831388cff880251c1b5bc38fbf6a283a0ee7c543709e8e9633d1"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:28a14ffc014e523498d077dfde12839b7be586ca8c3190e72e7167c1aea6ee4c"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1888,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "ef8f3d3357676ca2a1abf116a35e9b4204b241f751b10f3665d652afee908aba"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..c1f631a 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -20,17 +20,35 @@ def parse_args():
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
+     args = parser.parse_args()
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    for i in range(100):
++        writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..645ea93 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,15 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_103958-if0q7puu/files/requirements.txt b/wandb/run-20260421_103958-if0q7puu/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..caa1ea2c8ae112f7e5b026949b3425144d7c619b
--- /dev/null
+++ b/wandb/run-20260421_103958-if0q7puu/files/requirements.txt
@@ -0,0 +1,65 @@
+mpmath==1.3.0
+nvidia-nvtx-cu12==12.6.77
+pluggy==1.6.0
+charset-normalizer==3.4.7
+numpy==2.0.2
+Markdown==3.9
+protobuf==6.33.6
+Werkzeug==3.1.8
+tomli==2.4.1
+GitPython==3.1.46
+nvidia-cuda-cupti-cu12==12.6.80
+MarkupSafe==3.0.3
+certifi==2026.2.25
+nvidia-cublas-cu12==12.6.4.1
+nvidia-cusparselt-cu12==0.6.3
+networkx==3.2.1
+wandb==0.26.0
+tensorboard==2.20.0
+setuptools==65.5.0
+nvidia-cusparse-cu12==12.5.4.2
+gitdb==4.0.12
+pip==24.0
+Jinja2==3.1.6
+pydantic==2.13.3
+coverage==7.10.7
+sentry-sdk==2.58.0
+exceptiongroup==1.3.1
+sympy==1.14.0
+annotated-types==0.7.0
+absl-py==2.3.1
+smmap==5.0.3
+importlib_metadata==8.7.1
+idna==3.11
+pillow==11.3.0
+tensorboard-data-server==0.7.2
+cloudpickle==3.1.2
+platformdirs==4.4.0
+pytest==7.4.4
+nvidia-curand-cu12==10.3.7.77
+fsspec==2025.10.0
+click==8.1.8
+nvidia-cuda-nvrtc-cu12==12.6.77
+gymnasium==0.29.1
+nvidia-nccl-cu12==2.26.2
+urllib3==2.6.3
+packaging==26.0
+zipp==3.23.1
+nvidia-cufile-cu12==1.11.1.6
+nvidia-cudnn-cu12==9.5.1.17
+triton==3.3.1
+filelock==3.19.1
+Farama-Notifications==0.0.4
+grpcio==1.80.0
+iniconfig==2.1.0
+typing_extensions==4.15.0
+PyYAML==6.0.3
+nvidia-cusolver-cu12==11.7.1.2
+nvidia-cuda-runtime-cu12==12.6.77
+requests==2.32.5
+pydantic_core==2.46.3
+torch==2.7.1
+nvidia-cufft-cu12==11.3.0.4
+eval_type_backport==0.3.1
+typing-inspection==0.4.2
+nvidia-nvjitlink-cu12==12.6.85
diff --git a/wandb/run-20260421_103958-if0q7puu/files/runs/CartPole-v1_ppo_1_1776760603/events.out.tfevents.1776760800.BL-LFERNANDEZ.local.eurecat.org.19762.0 b/wandb/run-20260421_103958-if0q7puu/files/runs/CartPole-v1_ppo_1_1776760603/events.out.tfevents.1776760800.BL-LFERNANDEZ.local.eurecat.org.19762.0
new file mode 100644
index 0000000000000000000000000000000000000000..97e40b24fbd01af6692993a1753b1c493f1ea078
--- /dev/null
+++ b/wandb/run-20260421_103958-if0q7puu/files/runs/CartPole-v1_ppo_1_1776760603/events.out.tfevents.1776760800.BL-LFERNANDEZ.local.eurecat.org.19762.0
@@ -0,0 +1,3 @@
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+oid sha256:b556eab8c87d3450e4b7e3d8b474d2181818544a1d345d5d9baeac21dbec3022
+size 5084
diff --git a/wandb/run-20260421_103958-if0q7puu/files/wandb-metadata.json b/wandb/run-20260421_103958-if0q7puu/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..1750205f6fe163b467005a9f05b747548fbc65a8
--- /dev/null
+++ b/wandb/run-20260421_103958-if0q7puu/files/wandb-metadata.json
@@ -0,0 +1,31 @@
+{
+  "os": "Linux-6.17.0-20-generic-x86_64-with-glibc2.39",
+  "python": "CPython 3.9.5",
+  "startedAt": "2026-04-21T08:39:58.879224Z",
+  "args": [
+    "--track"
+  ],
+  "program": "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py",
+  "codePath": "ppo.py",
+  "codePathLocal": "ppo.py",
+  "git": {
+    "remote": "https://github.com/loisonchambers/Unit-8-RL-course.git",
+    "commit": "e1dc52c8934ce180fef18fbdf53a72281f134a52"
+  },
+  "email": "loisfernandezcmp@gmail.com",
+  "root": "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8",
+  "host": "BL-LFERNANDEZ.local.eurecat.org",
+  "executable": "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/bin/python",
+  "cpu_count": 12,
+  "cpu_count_logical": 14,
+  "disk": {
+    "/": {
+      "total": "499680116736",
+      "used": "139940552704"
+    }
+  },
+  "memory": {
+    "total": "33076023296"
+  },
+  "writerId": "c19uwkemcec75pa3bb2fpxuplfr1n5ol"
+}
\ No newline at end of file
diff --git a/wandb/run-20260421_103958-if0q7puu/files/wandb-summary.json b/wandb/run-20260421_103958-if0q7puu/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..3d24c081851bb1e52448d07741713ff197e34046
--- /dev/null
+++ b/wandb/run-20260421_103958-if0q7puu/files/wandb-summary.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/wandb/run-20260421_103958-if0q7puu/logs/debug-core.log b/wandb/run-20260421_103958-if0q7puu/logs/debug-core.log
new file mode 100644
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--- /dev/null
+++ b/wandb/run-20260421_103958-if0q7puu/logs/debug-core.log
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diff --git a/wandb/run-20260421_103958-if0q7puu/logs/debug-internal.log b/wandb/run-20260421_103958-if0q7puu/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..c51d26ebf4c1852bba2db45d11b6ea832fec7183
--- /dev/null
+++ b/wandb/run-20260421_103958-if0q7puu/logs/debug-internal.log
@@ -0,0 +1,21 @@
+{"time":"2026-04-21T10:39:59.276213744+02:00","level":"INFO","msg":"wandb-core"}
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+{"time":"2026-04-21T10:40:05.834223879+02:00","level":"INFO","msg":"filestream: sending request","total_files":0,"uploaded_len":1}
+{"time":"2026-04-21T10:40:06.143090348+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
+{"time":"2026-04-21T10:40:10.154792677+02:00","level":"WARN","msg":"tensorboard: no root directory","error":"timed out after 10s"}
+{"time":"2026-04-21T10:40:10.154968598+02:00","level":"INFO","msg":"tensorboard: tracking new log directory","rootDir":{},"logDir":"LocalOrCloudPath(LocalPath=\"/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/runs/CartPole-v1_ppo_1_1776760603\")","namespace":""}
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+{"time":"2026-04-21T10:40:11.292581156+02:00","level":"INFO","msg":"filestream: sending request","total_files":2,"history_offset":0,"history_lines":100,"uploaded_len":7,"complete":true,"exit_code":0}
+{"time":"2026-04-21T10:40:11.669471033+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
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diff --git a/wandb/run-20260421_103958-if0q7puu/logs/debug.log b/wandb/run-20260421_103958-if0q7puu/logs/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..a48b652e65dde2260748ec1cc698d782d21ba4a6
--- /dev/null
+++ b/wandb/run-20260421_103958-if0q7puu/logs/debug.log
@@ -0,0 +1,22 @@
+2026-04-21 10:39:58,881 INFO    MainThread:19762 [wandb_setup.py:_flush():81] Current SDK version is 0.26.0
+2026-04-21 10:39:58,882 INFO    MainThread:19762 [wandb_setup.py:_flush():81] Configure stats pid to 19762
+2026-04-21 10:39:58,882 INFO    MainThread:19762 [wandb_setup.py:_flush():81] Loading settings from environment variables
+2026-04-21 10:39:58,882 INFO    MainThread:19762 [wandb_init.py:setup_run_log_directory():721] Logging user logs to /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/wandb/run-20260421_103958-if0q7puu/logs/debug.log
+2026-04-21 10:39:58,882 INFO    MainThread:19762 [wandb_init.py:setup_run_log_directory():722] Logging internal logs to /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/wandb/run-20260421_103958-if0q7puu/logs/debug-internal.log
+2026-04-21 10:39:58,961 INFO    MainThread:19762 [wandb_init.py:init():848] calling init triggers
+2026-04-21 10:39:58,961 INFO    MainThread:19762 [wandb_init.py:init():853] wandb.init called with sweep_config: {}
+config: {'exp_name': 'ppo', 'gym_id': 'CartPole-v1', 'learning_rate': 0.00025, 'seed': 1, 'total_timesteps': 25000, 'torch deterministic': True, 'cuda': True, 'track': True, 'wandb_project_name': 'cleanRL', 'wandb_entity': None, '_wandb': {'code_path': 'code/ppo.py'}}
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diff --git a/wandb/run-20260421_103958-if0q7puu/run-if0q7puu.wandb b/wandb/run-20260421_103958-if0q7puu/run-if0q7puu.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..0c83d633372a63e3028199cbfc0dedfbea08ccf8
Binary files /dev/null and b/wandb/run-20260421_103958-if0q7puu/run-if0q7puu.wandb differ
diff --git a/wandb/run-20260421_120544-os0yjzp0/files/code/ppo.py b/wandb/run-20260421_120544-os0yjzp0/files/code/ppo.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc9a78b1a8c1c07988030d91b74a2f5d9e186e98
--- /dev/null
+++ b/wandb/run-20260421_120544-os0yjzp0/files/code/ppo.py
@@ -0,0 +1,95 @@
+import argparse
+import os
+from distutils.util import strtobool
+import time
+from torch.utils.tensorboard import SummaryWriter
+import torch
+import random
+import numpy as np
+import gym
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+        help='the name of this experiment')
+    parser.add_argument('--gym-id', type=str, default="CartPole-v1",
+        help='the id of the gym environment')
+    parser.add_argument('--learning-rate', type=float, default=2.5e-4,
+        help='the learning rate of the optimizer')
+    parser.add_argument('--seed', type=int, default=1,
+        help='seed of the experiment')
+    parser.add_argument('--total-timesteps', type=int, default=25000,
+        help='total timesteps of the experiments')
+    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+    parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, cuda will not be enabled by default')
+    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
+        help='if toggled, this experiment will be tracked with Weights and Biases')
+    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
+        help="the wandb's project name")
+    parser.add_argument('--wandb-entity', type=str, default=None,
+        help="the entity (team) of wandb's project")
+    args = parser.parse_args()
+    return args
+
+if __name__ == "__main__":
+    args = parse_args()
+    print(args)
+    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+    if args.track:
+        import wandb
+
+        wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            sync_tensorboard=True,
+            config=vars(args),
+            name=run_name,
+            monitor_gym=True,
+            save_code=True,
+        )
+    writer =SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
+    )
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
+
+    env = gym.make("CartPole-v1")
+    env = gym.wrappers.RecordEpisodeStatistics(env)
+    env = gym.wrappers.RecordVideo(env, "videos", episode_trigger=lambda t: t % 100 == 0)
+    observation = env.reset()
+    episodic_return = 0
+    for _ in range(200):
+        action = env.action_space.sample()
+        observation, reward, done, info = env.step(action)
+        episodic_return += reward
+        if done:
+            observation = env.reset()
+            print(f"episodic return: {episodic_return}")
+            episodic_return = 0
+    env.close()
+
+    def make_env(gym_id):
+        def thunk():
+            env = gym.make(gym_id)
+            env = gym.wrappers.RecordEpisodeStatistics(env)
+            env = gym.wrappers.RecordVideo(env, "videos", episode_trigger=lambda t: t % 100 == 0)
+            return env
+        return thunk
+    
+    envs = gym.vector.SyncVectorEnv([make_env(args.gym_id)])
+    observation = envs.reset()
+    for _ in range(200):
+        action = envs.action_space.sample()
+        observation, reward, done, info = envs.step(action)
+        for item in info:
+            if "episode" in item.keys():
+                print(f"episodic return {item['episode']['r']}")
+                # NOTE: there is no 'observation =env.reset() anymore
diff --git a/wandb/run-20260421_120544-os0yjzp0/files/config.yaml b/wandb/run-20260421_120544-os0yjzp0/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8798c01d43660ed1e69e3bf7d3780a1d31ded85e
--- /dev/null
+++ b/wandb/run-20260421_120544-os0yjzp0/files/config.yaml
@@ -0,0 +1,65 @@
+_wandb:
+    value:
+        cli_version: 0.26.0
+        code_path: code/ppo.py
+        e:
+            1qi4xmw2bs4pu4jv6wo9zlil689lnyw8:
+                args:
+                    - --track
+                codePath: ppo.py
+                codePathLocal: ppo.py
+                cpu_count: 12
+                cpu_count_logical: 14
+                disk:
+                    /:
+                        total: "499680116736"
+                        used: "141700804608"
+                email: loisfernandezcmp@gmail.com
+                executable: /home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/bin/python
+                git:
+                    commit: e1dc52c8934ce180fef18fbdf53a72281f134a52
+                    remote: https://github.com/loisonchambers/Unit-8-RL-course.git
+                host: BL-LFERNANDEZ.local.eurecat.org
+                memory:
+                    total: "33076023296"
+                os: Linux-6.17.0-20-generic-x86_64-with-glibc2.39
+                program: /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py
+                python: CPython 3.9.5
+                root: /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8
+                startedAt: "2026-04-21T10:05:44.025476Z"
+                writerId: 1qi4xmw2bs4pu4jv6wo9zlil689lnyw8
+        m: []
+        python_version: 3.9.5
+        t:
+            "1":
+                - 1
+            "2":
+                - 1
+            "3":
+                - 13
+                - 16
+                - 35
+            "4": 3.9.5
+            "5": 0.26.0
+            "12": 0.26.0
+            "13": linux-x86_64
+cuda:
+    value: true
+exp_name:
+    value: ppo
+gym_id:
+    value: CartPole-v1
+learning_rate:
+    value: 0.00025
+seed:
+    value: 1
+torch_deterministic:
+    value: true
+total_timesteps:
+    value: 25000
+track:
+    value: true
+wandb_entity:
+    value: null
+wandb_project_name:
+    value: cleanRL
diff --git a/wandb/run-20260421_120544-os0yjzp0/files/diff.patch b/wandb/run-20260421_120544-os0yjzp0/files/diff.patch
new file mode 100644
index 0000000000000000000000000000000000000000..b71497b74e1da40de2302924b4b34456e6bfb870
--- /dev/null
+++ b/wandb/run-20260421_120544-os0yjzp0/files/diff.patch
@@ -0,0 +1,1095 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..4c3cf56 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
++    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"},
++    {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"},
++]
++
++[[package]]
++name = "charset-normalizer"
++version = "3.4.7"
++description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
++optional = false
++python-versions = ">=3.7"
++files = [
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++
++[[package]]
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++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "eval_type_backport-0.3.1-py3-none-any.whl", hash = "sha256:279ab641905e9f11129f56a8a78f493518515b83402b860f6f06dd7c011fdfa8"},
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++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++smmap = ">=3.0.1,<6"
++
++[[package]]
++name = "gitpython"
++version = "3.1.46"
++description = "GitPython is a Python library used to interact with Git repositories"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++gitdb = ">=4.0.1,<5"
++typing-extensions = {version = ">=3.10.0.2", markers = "python_version < \"3.10\""}
++
++[package.extras]
++doc = ["sphinx (>=7.1.2,<7.2)", "sphinx-autodoc-typehints", "sphinx_rtd_theme"]
++test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy (==1.18.2)", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
++
+ [[package]]
+ name = "grpcio"
+ version = "1.80.0"
+@@ -303,6 +524,45 @@ typing-extensions = ">=4.12,<5.0"
+ [package.extras]
+ protobuf = ["grpcio-tools (>=1.80.0)"]
+ 
++[[package]]
++name = "gym"
++version = "0.25.2"
++description = "Gym: A universal API for reinforcement learning environments"
++optional = false
++python-versions = ">=3.6"
++files = [
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++]
++
++[package.dependencies]
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++gym_notices = ">=0.0.4"
++importlib_metadata = {version = ">=4.8.0", markers = "python_version < \"3.10\""}
++numpy = ">=1.18.0"
++
++[package.extras]
++accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"]
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++mujoco-py = ["mujoco_py (>=2.1,<2.2)"]
++other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "opencv-python (>=3.0)"]
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++toy-text = ["pygame (==2.1.0)"]
++
++[[package]]
++name = "gym-notices"
++version = "0.1.0"
++description = "Notices for gym"
++optional = false
++python-versions = "*"
++files = [
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++]
++
+ [[package]]
+ name = "gymnasium"
+ version = "0.29.1"
+@@ -334,6 +594,36 @@ other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-pyt
+ testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
+ toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+ 
++[[package]]
++name = "idna"
++version = "3.11"
++description = "Internationalized Domain Names in Applications (IDNA)"
++optional = false
++python-versions = ">=3.8"
++files = [
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++
++[package.extras]
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++
++[[package]]
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++
+ [[package]]
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+ version = "8.7.1"
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+ 
+ [[package]]
+ name = "numpy"
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++version = "1.23.5"
++description = "NumPy is the fundamental package for array computing with Python."
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++python-versions = ">=3.8"
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++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1995,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "89750cd6ebb50c516550a1896508559bba968e0ca72314d4dfa46ba5469fa298"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..bc9a78b 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -3,6 +3,10 @@ import os
+ from distutils.util import strtobool
+ import time
+ from torch.utils.tensorboard import SummaryWriter
++import torch
++import random
++import numpy as np
++import gym
+ 
+ def parse_args():
+     parser = argparse.ArgumentParser()
+@@ -16,21 +20,76 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
+     args = parser.parse_args()
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    env = gym.make("CartPole-v1")
++    env = gym.wrappers.RecordEpisodeStatistics(env)
++    env = gym.wrappers.RecordVideo(env, "videos", episode_trigger=lambda t: t % 100 == 0)
++    observation = env.reset()
++    episodic_return = 0
++    for _ in range(200):
++        action = env.action_space.sample()
++        observation, reward, done, info = env.step(action)
++        episodic_return += reward
++        if done:
++            observation = env.reset()
++            print(f"episodic return: {episodic_return}")
++            episodic_return = 0
++    env.close()
++
++    def make_env(gym_id):
++        def thunk():
++            env = gym.make(gym_id)
++            env = gym.wrappers.RecordEpisodeStatistics(env)
++            env = gym.wrappers.RecordVideo(env, "videos", episode_trigger=lambda t: t % 100 == 0)
++            return env
++        return thunk
++    
++    envs = gym.vector.SyncVectorEnv([make_env(args.gym_id)])
++    observation = envs.reset()
++    for _ in range(200):
++        action = envs.action_space.sample()
++        observation, reward, done, info = envs.step(action)
++        for item in info:
++            if "episode" in item.keys():
++                print(f"episodic return {item['episode']['r']}")
++                # NOTE: there is no 'observation =env.reset() anymore
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..eb4a63c 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.25.2"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_120544-os0yjzp0/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch b/wandb/run-20260421_120544-os0yjzp0/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
new file mode 100644
index 0000000000000000000000000000000000000000..b71497b74e1da40de2302924b4b34456e6bfb870
--- /dev/null
+++ b/wandb/run-20260421_120544-os0yjzp0/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
@@ -0,0 +1,1095 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..4c3cf56 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
++    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"},
++    {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"},
++]
++
++[[package]]
++name = "charset-normalizer"
++version = "3.4.7"
++description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "charset_normalizer-3.4.7-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:cdd68a1fb318e290a2077696b7eb7a21a49163c455979c639bf5a5dcdc46617d"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e17b8d5d6a8c47c85e68ca8379def1303fd360c3e22093a807cd34a71cd082b8"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:511ef87c8aec0783e08ac18565a16d435372bc1ac25a91e6ac7f5ef2b0bff790"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:007d05ec7321d12a40227aae9e2bc6dca73f3cb21058999a1df9e193555a9dcc"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:cf29836da5119f3c8a8a70667b0ef5fdca3bb12f80fd06487cfa575b3909b393"},
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++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:d560742f3c0d62afaccf9f41fe485ed69bd7661a241f86a3ef0f0fb8b1a397af"},
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++
++[[package]]
++name = "click"
++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
++optional = false
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++
++[package.dependencies]
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++
++[[package]]
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++description = "GitPython is a Python library used to interact with Git repositories"
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++
++[package.extras]
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++
+ [[package]]
+ name = "grpcio"
+ version = "1.80.0"
+@@ -303,6 +524,45 @@ typing-extensions = ">=4.12,<5.0"
+ [package.extras]
+ protobuf = ["grpcio-tools (>=1.80.0)"]
+ 
++[[package]]
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++[package.dependencies]
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++
++[package.extras]
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++
++[[package]]
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++optional = false
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++
+ [[package]]
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+@@ -334,6 +594,36 @@ other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-pyt
+ testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
+ toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+ 
++[[package]]
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++    {file = "pyyaml-6.0.3-cp39-cp39-win_amd64.whl", hash = "sha256:2e71d11abed7344e42a8849600193d15b6def118602c4c176f748e4583246007"},
++    {file = "pyyaml-6.0.3.tar.gz", hash = "sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f"},
++]
++
++[[package]]
++name = "requests"
++version = "2.32.5"
++description = "Python HTTP for Humans."
+ optional = false
+ python-versions = ">=3.9"
+ files = [
+-    {file = "setuptools-82.0.1-py3-none-any.whl", hash = "sha256:a59e362652f08dcd477c78bb6e7bd9d80a7995bc73ce773050228a348ce2e5bb"},
+-    {file = "setuptools-82.0.1.tar.gz", hash = "sha256:7d872682c5d01cfde07da7bccc7b65469d3dca203318515ada1de5eda35efbf9"},
++    {file = "requests-2.32.5-py3-none-any.whl", hash = "sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6"},
++    {file = "requests-2.32.5.tar.gz", hash = "sha256:dbba0bac56e100853db0ea71b82b4dfd5fe2bf6d3754a8893c3af500cec7d7cf"},
+ ]
+ 
++[package.dependencies]
++certifi = ">=2017.4.17"
++charset_normalizer = ">=2,<4"
++idna = ">=2.5,<4"
++urllib3 = ">=1.21.1,<3"
++
+ [package.extras]
+-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.13.0)"]
+-core = ["importlib_metadata (>=6)", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more_itertools", "more_itertools (>=8.8)", "packaging (>=24.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
+-cover = ["pytest-cov"]
+-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+-enabler = ["pytest-enabler (>=2.2)"]
+-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+-type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.18.*)", "pytest-mypy"]
++socks = ["PySocks (>=1.5.6,!=1.5.7)"]
++use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
++
++[[package]]
++name = "sentry-sdk"
++version = "2.58.0"
++description = "Python client for Sentry (https://sentry.io)"
++optional = false
++python-versions = ">=3.6"
++files = [
++    {file = "sentry_sdk-2.58.0-py2.py3-none-any.whl", hash = "sha256:688d1c704ddecf382ea3326f21a67453d4caa95592d722b7c780a36a9d23109e"},
++    {file = "sentry_sdk-2.58.0.tar.gz", hash = "sha256:c1144d947352d54e5b7daa63596d9f848adf684989c06c4f5a659f0c85a18f6f"},
++]
++
++[package.dependencies]
++certifi = "*"
++urllib3 = ">=1.26.11"
++
++[package.extras]
++aiohttp = ["aiohttp (>=3.5)"]
++anthropic = ["anthropic (>=0.16)"]
++arq = ["arq (>=0.23)"]
++asyncio = ["httpcore[asyncio] (==1.*)"]
++asyncpg = ["asyncpg (>=0.23)"]
++beam = ["apache-beam (>=2.12)"]
++bottle = ["bottle (>=0.12.13)"]
++celery = ["celery (>=3)"]
++celery-redbeat = ["celery-redbeat (>=2)"]
++chalice = ["chalice (>=1.16.0)"]
++clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
++django = ["django (>=1.8)"]
++falcon = ["falcon (>=1.4)"]
++fastapi = ["fastapi (>=0.79.0)"]
++flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
++google-genai = ["google-genai (>=1.29.0)"]
++grpcio = ["grpcio (>=1.21.1)", "protobuf (>=3.8.0)"]
++http2 = ["httpcore[http2] (==1.*)"]
++httpx = ["httpx (>=0.16.0)"]
++huey = ["huey (>=2)"]
++huggingface-hub = ["huggingface_hub (>=0.22)"]
++langchain = ["langchain (>=0.0.210)"]
++langgraph = ["langgraph (>=0.6.6)"]
++launchdarkly = ["launchdarkly-server-sdk (>=9.8.0)"]
++litellm = ["litellm (>=1.77.5,!=1.82.7,!=1.82.8)"]
++litestar = ["litestar (>=2.0.0)"]
++loguru = ["loguru (>=0.5)"]
++mcp = ["mcp (>=1.15.0)"]
++openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
++openfeature = ["openfeature-sdk (>=0.7.1)"]
++opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
++opentelemetry-experimental = ["opentelemetry-distro"]
++opentelemetry-otlp = ["opentelemetry-distro[otlp] (>=0.35b0)"]
++pure-eval = ["asttokens", "executing", "pure_eval"]
++pydantic-ai = ["pydantic-ai (>=1.0.0)"]
++pymongo = ["pymongo (>=3.1)"]
++pyspark = ["pyspark (>=2.4.4)"]
++quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
++rq = ["rq (>=0.6)"]
++sanic = ["sanic (>=0.8)"]
++sqlalchemy = ["sqlalchemy (>=1.2)"]
++starlette = ["starlette (>=0.19.1)"]
++starlite = ["starlite (>=1.48)"]
++statsig = ["statsig (>=0.55.3)"]
++tornado = ["tornado (>=6)"]
++unleash = ["UnleashClient (>=6.0.1)"]
++
++[[package]]
++name = "setuptools"
++version = "65.5.0"
++description = "Easily download, build, install, upgrade, and uninstall Python packages"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "setuptools-65.5.0-py3-none-any.whl", hash = "sha256:f62ea9da9ed6289bfe868cd6845968a2c854d1427f8548d52cae02a42b4f0356"},
++    {file = "setuptools-65.5.0.tar.gz", hash = "sha256:512e5536220e38146176efb833d4a62aa726b7bbff82cfbc8ba9eaa3996e0b17"},
++]
++
++[package.extras]
++docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
++testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
++testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
++
++[[package]]
++name = "smmap"
++version = "5.0.3"
++description = "A pure Python implementation of a sliding window memory map manager"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "smmap-5.0.3-py3-none-any.whl", hash = "sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f"},
++    {file = "smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c"},
++]
+ 
+ [[package]]
+ name = "sympy"
+@@ -1197,6 +1881,81 @@ files = [
+     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+ ]
+ 
++[[package]]
++name = "typing-inspection"
++version = "0.4.2"
++description = "Runtime typing introspection tools"
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
++    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
++]
++
++[package.dependencies]
++typing-extensions = ">=4.12.0"
++
++[[package]]
++name = "urllib3"
++version = "2.6.3"
++description = "HTTP library with thread-safe connection pooling, file post, and more."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
++brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"]
++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:1ece94a2a5eda1d0e3a2d8a2fd28aa0187705d6efa5ac4c0b8680083583b7ec1"},
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:92f6f303fe2af50e3f711833a835150f9b4d8082874bfd9868cf15491ea2947e"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:a17aae051a31831388cff880251c1b5bc38fbf6a283a0ee7c543709e8e9633d1"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:28a14ffc014e523498d077dfde12839b7be586ca8c3190e72e7167c1aea6ee4c"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1995,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "89750cd6ebb50c516550a1896508559bba968e0ca72314d4dfa46ba5469fa298"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..bc9a78b 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -3,6 +3,10 @@ import os
+ from distutils.util import strtobool
+ import time
+ from torch.utils.tensorboard import SummaryWriter
++import torch
++import random
++import numpy as np
++import gym
+ 
+ def parse_args():
+     parser = argparse.ArgumentParser()
+@@ -16,21 +20,76 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
+     args = parser.parse_args()
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    env = gym.make("CartPole-v1")
++    env = gym.wrappers.RecordEpisodeStatistics(env)
++    env = gym.wrappers.RecordVideo(env, "videos", episode_trigger=lambda t: t % 100 == 0)
++    observation = env.reset()
++    episodic_return = 0
++    for _ in range(200):
++        action = env.action_space.sample()
++        observation, reward, done, info = env.step(action)
++        episodic_return += reward
++        if done:
++            observation = env.reset()
++            print(f"episodic return: {episodic_return}")
++            episodic_return = 0
++    env.close()
++
++    def make_env(gym_id):
++        def thunk():
++            env = gym.make(gym_id)
++            env = gym.wrappers.RecordEpisodeStatistics(env)
++            env = gym.wrappers.RecordVideo(env, "videos", episode_trigger=lambda t: t % 100 == 0)
++            return env
++        return thunk
++    
++    envs = gym.vector.SyncVectorEnv([make_env(args.gym_id)])
++    observation = envs.reset()
++    for _ in range(200):
++        action = envs.action_space.sample()
++        observation, reward, done, info = envs.step(action)
++        for item in info:
++            if "episode" in item.keys():
++                print(f"episodic return {item['episode']['r']}")
++                # NOTE: there is no 'observation =env.reset() anymore
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..eb4a63c 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.25.2"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_120544-os0yjzp0/files/output.log b/wandb/run-20260421_120544-os0yjzp0/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..03e34120ed5affd164b138cca19e4e6fbb9e2e3c
--- /dev/null
+++ b/wandb/run-20260421_120544-os0yjzp0/files/output.log
@@ -0,0 +1,24 @@
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py:317: DeprecationWarning: WARN: Initializing wrapper in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.
+  deprecation(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/step_api_compatibility.py:39: DeprecationWarning: WARN: Initializing environment in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.
+  deprecation(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/record_video.py:78: UserWarning: WARN: Overwriting existing videos at /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/videos folder (try specifying a different `video_folder` for the `RecordVideo` wrapper if this is not desired)
+  logger.warn(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/monitoring/video_recorder.py:78: DeprecationWarning: WARN: Recording ability for environment CartPole-v1 initialized with `render_mode=None` is marked as deprecated and will be removed in the future.
+  logger.deprecation(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py:43: DeprecationWarning: WARN: The argument mode in render method is deprecated; use render_mode during environment initialization instead.
+See here for more information: https://www.gymlibrary.ml/content/api/
+  deprecation(
+episodic return: 27.0
+episodic return: 21.0
+episodic return: 45.0
+episodic return: 42.0
+episodic return: 14.0
+episodic return: 12.0
+episodic return: 19.0
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/vector/vector_env.py:56: DeprecationWarning: WARN: Initializing vector env in old step API which returns one bool array instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.
+  deprecation(
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py", line 93, in <module>
+    if "episode" in item.keys():
+AttributeError: 'str' object has no attribute 'keys'
diff --git a/wandb/run-20260421_120544-os0yjzp0/files/requirements.txt b/wandb/run-20260421_120544-os0yjzp0/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3f020f4ecd61d8f6a97ef8435a2e387b50f5a3c3
--- /dev/null
+++ b/wandb/run-20260421_120544-os0yjzp0/files/requirements.txt
@@ -0,0 +1,70 @@
+mpmath==1.3.0
+nvidia-nvtx-cu12==12.6.77
+pluggy==1.6.0
+charset-normalizer==3.4.7
+Markdown==3.9
+protobuf==6.33.6
+Werkzeug==3.1.8
+gym-notices==0.1.0
+tomli==2.4.1
+GitPython==3.1.46
+nvidia-cuda-cupti-cu12==12.6.80
+MarkupSafe==3.0.3
+certifi==2026.2.25
+nvidia-cublas-cu12==12.6.4.1
+nvidia-cusparselt-cu12==0.6.3
+networkx==3.2.1
+wandb==0.26.0
+tensorboard==2.20.0
+setuptools==65.5.0
+nvidia-cusparse-cu12==12.5.4.2
+gitdb==4.0.12
+pip==24.0
+Jinja2==3.1.6
+pydantic==2.13.3
+coverage==7.10.7
+sentry-sdk==2.58.0
+exceptiongroup==1.3.1
+sympy==1.14.0
+annotated-types==0.7.0
+absl-py==2.3.1
+smmap==5.0.3
+importlib_metadata==8.7.1
+idna==3.11
+pillow==11.3.0
+tensorboard-data-server==0.7.2
+cloudpickle==3.1.2
+platformdirs==4.4.0
+gym==0.25.2
+pytest==7.4.4
+nvidia-curand-cu12==10.3.7.77
+fsspec==2025.10.0
+click==8.1.8
+nvidia-cuda-nvrtc-cu12==12.6.77
+gymnasium==0.29.1
+numpy==1.23.5
+imageio-ffmpeg==0.6.0
+nvidia-nccl-cu12==2.26.2
+urllib3==2.6.3
+packaging==26.0
+zipp==3.23.1
+nvidia-cufile-cu12==1.11.1.6
+nvidia-cudnn-cu12==9.5.1.17
+triton==3.3.1
+filelock==3.19.1
+Farama-Notifications==0.0.4
+grpcio==1.80.0
+iniconfig==2.1.0
+typing_extensions==4.15.0
+PyYAML==6.0.3
+nvidia-cusolver-cu12==11.7.1.2
+nvidia-cuda-runtime-cu12==12.6.77
+pygame==2.6.1
+requests==2.32.5
+pydantic_core==2.46.3
+torch==2.7.1
+nvidia-cufft-cu12==11.3.0.4
+eval_type_backport==0.3.1
+wheel==0.38.4
+typing-inspection==0.4.2
+nvidia-nvjitlink-cu12==12.6.85
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index 0000000000000000000000000000000000000000..357d49f773e37c97f44a44db3f1faabb90828955
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fe38d20ce1da58e3db7c61d6e468f058e96899863695087cdf1342a10ff67936
+size 386
diff --git a/wandb/run-20260421_120544-os0yjzp0/files/wandb-metadata.json b/wandb/run-20260421_120544-os0yjzp0/files/wandb-metadata.json
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+++ b/wandb/run-20260421_120544-os0yjzp0/files/wandb-metadata.json
@@ -0,0 +1,31 @@
+{
+  "os": "Linux-6.17.0-20-generic-x86_64-with-glibc2.39",
+  "python": "CPython 3.9.5",
+  "startedAt": "2026-04-21T10:05:44.025476Z",
+  "args": [
+    "--track"
+  ],
+  "program": "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py",
+  "codePath": "ppo.py",
+  "codePathLocal": "ppo.py",
+  "git": {
+    "remote": "https://github.com/loisonchambers/Unit-8-RL-course.git",
+    "commit": "e1dc52c8934ce180fef18fbdf53a72281f134a52"
+  },
+  "email": "loisfernandezcmp@gmail.com",
+  "root": "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8",
+  "host": "BL-LFERNANDEZ.local.eurecat.org",
+  "executable": "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/bin/python",
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diff --git a/wandb/run-20260421_120544-os0yjzp0/logs/debug.log b/wandb/run-20260421_120544-os0yjzp0/logs/debug.log
new file mode 100644
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+2026-04-21 12:05:44,050 INFO    MainThread:49910 [wandb_init.py:init():853] wandb.init called with sweep_config: {}
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diff --git a/wandb/run-20260421_120544-os0yjzp0/run-os0yjzp0.wandb b/wandb/run-20260421_120544-os0yjzp0/run-os0yjzp0.wandb
new file mode 100644
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diff --git a/wandb/run-20260421_120843-tuj6f8rr/files/code/ppo.py b/wandb/run-20260421_120843-tuj6f8rr/files/code/ppo.py
new file mode 100644
index 0000000000000000000000000000000000000000..8837ac4c5db495e546a31b017be36dc86c4ae3ae
--- /dev/null
+++ b/wandb/run-20260421_120843-tuj6f8rr/files/code/ppo.py
@@ -0,0 +1,99 @@
+import argparse
+import os
+from distutils.util import strtobool
+import time
+from torch.utils.tensorboard import SummaryWriter
+import torch
+import random
+import numpy as np
+import gym
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+        help='the name of this experiment')
+    parser.add_argument('--gym-id', type=str, default="CartPole-v1",
+        help='the id of the gym environment')
+    parser.add_argument('--learning-rate', type=float, default=2.5e-4,
+        help='the learning rate of the optimizer')
+    parser.add_argument('--seed', type=int, default=1,
+        help='seed of the experiment')
+    parser.add_argument('--total-timesteps', type=int, default=25000,
+        help='total timesteps of the experiments')
+    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+    parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, cuda will not be enabled by default')
+    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
+        help='if toggled, this experiment will be tracked with Weights and Biases')
+    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
+        help="the wandb's project name")
+    parser.add_argument('--wandb-entity', type=str, default=None,
+        help="the entity (team) of wandb's project")
+    args = parser.parse_args()
+    return args
+
+if __name__ == "__main__":
+    args = parse_args()
+    print(args)
+    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+    if args.track:
+        import wandb
+
+        wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            sync_tensorboard=True,
+            config=vars(args),
+            name=run_name,
+            monitor_gym=True,
+            save_code=True,
+        )
+    writer =SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
+    )
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
+
+    env = gym.make("CartPole-v1")
+    env = gym.wrappers.RecordEpisodeStatistics(env)
+    env = gym.wrappers.RecordVideo(env, "videos", episode_trigger=lambda t: t % 100 == 0)
+    observation = env.reset()
+    episodic_return = 0
+    for _ in range(200):
+        action = env.action_space.sample()
+        observation, reward, done, info = env.step(action)
+        episodic_return += reward
+        if done:
+            observation = env.reset()
+            print(f"episodic return: {episodic_return}")
+            episodic_return = 0
+    env.close()
+
+    def make_env(gym_id):
+        def thunk():
+            env = gym.make(gym_id)
+            env = gym.wrappers.RecordEpisodeStatistics(env)
+            env = gym.wrappers.RecordVideo(env, "videos", episode_trigger=lambda t: t % 100 == 0)
+            return env
+        return thunk
+    
+envs = gym.vector.SyncVectorEnv([make_env(args.gym_id)])
+observation = envs.reset()
+for _ in range(200):
+    action = envs.action_space.sample()
+    observation, reward, done, info = envs.step(action)
+    
+    # En SyncVectorEnv, info es un diccionario. 
+    # Buscamos la clave "episode" que contiene los datos de los entornos que terminaron.
+    if isinstance(info, dict) and "episode" in info:
+        # info["episode"] contiene un array de resultados para cada sub-entorno
+        for item in info["episode"]:
+            if item is not None:
+                print(f"episodic return {item['r']}")
diff --git a/wandb/run-20260421_120843-tuj6f8rr/files/config.yaml b/wandb/run-20260421_120843-tuj6f8rr/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..07397efe6e617235dce382007a4d4e69a4307624
--- /dev/null
+++ b/wandb/run-20260421_120843-tuj6f8rr/files/config.yaml
@@ -0,0 +1,65 @@
+_wandb:
+    value:
+        cli_version: 0.26.0
+        code_path: code/ppo.py
+        e:
+            1p7yp9r6vzvxue3oymx7owupcbk1n7z3:
+                args:
+                    - --track
+                codePath: ppo.py
+                codePathLocal: ppo.py
+                cpu_count: 12
+                cpu_count_logical: 14
+                disk:
+                    /:
+                        total: "499680116736"
+                        used: "141702451200"
+                email: loisfernandezcmp@gmail.com
+                executable: /home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/bin/python
+                git:
+                    commit: e1dc52c8934ce180fef18fbdf53a72281f134a52
+                    remote: https://github.com/loisonchambers/Unit-8-RL-course.git
+                host: BL-LFERNANDEZ.local.eurecat.org
+                memory:
+                    total: "33076023296"
+                os: Linux-6.17.0-20-generic-x86_64-with-glibc2.39
+                program: /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py
+                python: CPython 3.9.5
+                root: /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8
+                startedAt: "2026-04-21T10:08:43.120451Z"
+                writerId: 1p7yp9r6vzvxue3oymx7owupcbk1n7z3
+        m: []
+        python_version: 3.9.5
+        t:
+            "1":
+                - 1
+            "2":
+                - 1
+            "3":
+                - 13
+                - 16
+                - 35
+            "4": 3.9.5
+            "5": 0.26.0
+            "12": 0.26.0
+            "13": linux-x86_64
+cuda:
+    value: true
+exp_name:
+    value: ppo
+gym_id:
+    value: CartPole-v1
+learning_rate:
+    value: 0.00025
+seed:
+    value: 1
+torch_deterministic:
+    value: true
+total_timesteps:
+    value: 25000
+track:
+    value: true
+wandb_entity:
+    value: null
+wandb_project_name:
+    value: cleanRL
diff --git a/wandb/run-20260421_120843-tuj6f8rr/files/diff.patch b/wandb/run-20260421_120843-tuj6f8rr/files/diff.patch
new file mode 100644
index 0000000000000000000000000000000000000000..e05b57f88a0bf01b22493c4f505a0543c4cf6fd1
--- /dev/null
+++ b/wandb/run-20260421_120843-tuj6f8rr/files/diff.patch
@@ -0,0 +1,1099 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..4c3cf56 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
++    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"},
++    {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"},
++]
++
++[[package]]
++name = "charset-normalizer"
++version = "3.4.7"
++description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
++optional = false
++python-versions = ">=3.7"
++files = [
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++
++[[package]]
++name = "click"
++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++smmap = ">=3.0.1,<6"
++
++[[package]]
++name = "gitpython"
++version = "3.1.46"
++description = "GitPython is a Python library used to interact with Git repositories"
++optional = false
++python-versions = ">=3.7"
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+ 
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+ xmp = ["defusedxml"]
+ 
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++    {file = "pyyaml-6.0.3-cp39-cp39-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5ed875a24292240029e4483f9d4a4b8a1ae08843b9c54f43fcc11e404532a8a5"},
++    {file = "pyyaml-6.0.3-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0150219816b6a1fa26fb4699fb7daa9caf09eb1999f3b70fb6e786805e80375a"},
++    {file = "pyyaml-6.0.3-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:fa160448684b4e94d80416c0fa4aac48967a969efe22931448d853ada8baf926"},
++    {file = "pyyaml-6.0.3-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:27c0abcb4a5dac13684a37f76e701e054692a9b2d3064b70f5e4eb54810553d7"},
++    {file = "pyyaml-6.0.3-cp39-cp39-win32.whl", hash = "sha256:1ebe39cb5fc479422b83de611d14e2c0d3bb2a18bbcb01f229ab3cfbd8fee7a0"},
++    {file = "pyyaml-6.0.3-cp39-cp39-win_amd64.whl", hash = "sha256:2e71d11abed7344e42a8849600193d15b6def118602c4c176f748e4583246007"},
++    {file = "pyyaml-6.0.3.tar.gz", hash = "sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f"},
++]
++
++[[package]]
++name = "requests"
++version = "2.32.5"
++description = "Python HTTP for Humans."
+ optional = false
+ python-versions = ">=3.9"
+ files = [
+-    {file = "setuptools-82.0.1-py3-none-any.whl", hash = "sha256:a59e362652f08dcd477c78bb6e7bd9d80a7995bc73ce773050228a348ce2e5bb"},
+-    {file = "setuptools-82.0.1.tar.gz", hash = "sha256:7d872682c5d01cfde07da7bccc7b65469d3dca203318515ada1de5eda35efbf9"},
++    {file = "requests-2.32.5-py3-none-any.whl", hash = "sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6"},
++    {file = "requests-2.32.5.tar.gz", hash = "sha256:dbba0bac56e100853db0ea71b82b4dfd5fe2bf6d3754a8893c3af500cec7d7cf"},
+ ]
+ 
++[package.dependencies]
++certifi = ">=2017.4.17"
++charset_normalizer = ">=2,<4"
++idna = ">=2.5,<4"
++urllib3 = ">=1.21.1,<3"
++
+ [package.extras]
+-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.13.0)"]
+-core = ["importlib_metadata (>=6)", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more_itertools", "more_itertools (>=8.8)", "packaging (>=24.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
+-cover = ["pytest-cov"]
+-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+-enabler = ["pytest-enabler (>=2.2)"]
+-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+-type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.18.*)", "pytest-mypy"]
++socks = ["PySocks (>=1.5.6,!=1.5.7)"]
++use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
++
++[[package]]
++name = "sentry-sdk"
++version = "2.58.0"
++description = "Python client for Sentry (https://sentry.io)"
++optional = false
++python-versions = ">=3.6"
++files = [
++    {file = "sentry_sdk-2.58.0-py2.py3-none-any.whl", hash = "sha256:688d1c704ddecf382ea3326f21a67453d4caa95592d722b7c780a36a9d23109e"},
++    {file = "sentry_sdk-2.58.0.tar.gz", hash = "sha256:c1144d947352d54e5b7daa63596d9f848adf684989c06c4f5a659f0c85a18f6f"},
++]
++
++[package.dependencies]
++certifi = "*"
++urllib3 = ">=1.26.11"
++
++[package.extras]
++aiohttp = ["aiohttp (>=3.5)"]
++anthropic = ["anthropic (>=0.16)"]
++arq = ["arq (>=0.23)"]
++asyncio = ["httpcore[asyncio] (==1.*)"]
++asyncpg = ["asyncpg (>=0.23)"]
++beam = ["apache-beam (>=2.12)"]
++bottle = ["bottle (>=0.12.13)"]
++celery = ["celery (>=3)"]
++celery-redbeat = ["celery-redbeat (>=2)"]
++chalice = ["chalice (>=1.16.0)"]
++clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
++django = ["django (>=1.8)"]
++falcon = ["falcon (>=1.4)"]
++fastapi = ["fastapi (>=0.79.0)"]
++flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
++google-genai = ["google-genai (>=1.29.0)"]
++grpcio = ["grpcio (>=1.21.1)", "protobuf (>=3.8.0)"]
++http2 = ["httpcore[http2] (==1.*)"]
++httpx = ["httpx (>=0.16.0)"]
++huey = ["huey (>=2)"]
++huggingface-hub = ["huggingface_hub (>=0.22)"]
++langchain = ["langchain (>=0.0.210)"]
++langgraph = ["langgraph (>=0.6.6)"]
++launchdarkly = ["launchdarkly-server-sdk (>=9.8.0)"]
++litellm = ["litellm (>=1.77.5,!=1.82.7,!=1.82.8)"]
++litestar = ["litestar (>=2.0.0)"]
++loguru = ["loguru (>=0.5)"]
++mcp = ["mcp (>=1.15.0)"]
++openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
++openfeature = ["openfeature-sdk (>=0.7.1)"]
++opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
++opentelemetry-experimental = ["opentelemetry-distro"]
++opentelemetry-otlp = ["opentelemetry-distro[otlp] (>=0.35b0)"]
++pure-eval = ["asttokens", "executing", "pure_eval"]
++pydantic-ai = ["pydantic-ai (>=1.0.0)"]
++pymongo = ["pymongo (>=3.1)"]
++pyspark = ["pyspark (>=2.4.4)"]
++quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
++rq = ["rq (>=0.6)"]
++sanic = ["sanic (>=0.8)"]
++sqlalchemy = ["sqlalchemy (>=1.2)"]
++starlette = ["starlette (>=0.19.1)"]
++starlite = ["starlite (>=1.48)"]
++statsig = ["statsig (>=0.55.3)"]
++tornado = ["tornado (>=6)"]
++unleash = ["UnleashClient (>=6.0.1)"]
++
++[[package]]
++name = "setuptools"
++version = "65.5.0"
++description = "Easily download, build, install, upgrade, and uninstall Python packages"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "setuptools-65.5.0-py3-none-any.whl", hash = "sha256:f62ea9da9ed6289bfe868cd6845968a2c854d1427f8548d52cae02a42b4f0356"},
++    {file = "setuptools-65.5.0.tar.gz", hash = "sha256:512e5536220e38146176efb833d4a62aa726b7bbff82cfbc8ba9eaa3996e0b17"},
++]
++
++[package.extras]
++docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
++testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
++testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
++
++[[package]]
++name = "smmap"
++version = "5.0.3"
++description = "A pure Python implementation of a sliding window memory map manager"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "smmap-5.0.3-py3-none-any.whl", hash = "sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f"},
++    {file = "smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c"},
++]
+ 
+ [[package]]
+ name = "sympy"
+@@ -1197,6 +1881,81 @@ files = [
+     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+ ]
+ 
++[[package]]
++name = "typing-inspection"
++version = "0.4.2"
++description = "Runtime typing introspection tools"
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
++    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
++]
++
++[package.dependencies]
++typing-extensions = ">=4.12.0"
++
++[[package]]
++name = "urllib3"
++version = "2.6.3"
++description = "HTTP library with thread-safe connection pooling, file post, and more."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
++brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"]
++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:1ece94a2a5eda1d0e3a2d8a2fd28aa0187705d6efa5ac4c0b8680083583b7ec1"},
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:92f6f303fe2af50e3f711833a835150f9b4d8082874bfd9868cf15491ea2947e"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:a17aae051a31831388cff880251c1b5bc38fbf6a283a0ee7c543709e8e9633d1"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:28a14ffc014e523498d077dfde12839b7be586ca8c3190e72e7167c1aea6ee4c"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1995,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "89750cd6ebb50c516550a1896508559bba968e0ca72314d4dfa46ba5469fa298"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..8837ac4 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -3,6 +3,10 @@ import os
+ from distutils.util import strtobool
+ import time
+ from torch.utils.tensorboard import SummaryWriter
++import torch
++import random
++import numpy as np
++import gym
+ 
+ def parse_args():
+     parser = argparse.ArgumentParser()
+@@ -16,21 +20,80 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
+     args = parser.parse_args()
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    env = gym.make("CartPole-v1")
++    env = gym.wrappers.RecordEpisodeStatistics(env)
++    env = gym.wrappers.RecordVideo(env, "videos", episode_trigger=lambda t: t % 100 == 0)
++    observation = env.reset()
++    episodic_return = 0
++    for _ in range(200):
++        action = env.action_space.sample()
++        observation, reward, done, info = env.step(action)
++        episodic_return += reward
++        if done:
++            observation = env.reset()
++            print(f"episodic return: {episodic_return}")
++            episodic_return = 0
++    env.close()
++
++    def make_env(gym_id):
++        def thunk():
++            env = gym.make(gym_id)
++            env = gym.wrappers.RecordEpisodeStatistics(env)
++            env = gym.wrappers.RecordVideo(env, "videos", episode_trigger=lambda t: t % 100 == 0)
++            return env
++        return thunk
++    
++envs = gym.vector.SyncVectorEnv([make_env(args.gym_id)])
++observation = envs.reset()
++for _ in range(200):
++    action = envs.action_space.sample()
++    observation, reward, done, info = envs.step(action)
++    
++    # En SyncVectorEnv, info es un diccionario. 
++    # Buscamos la clave "episode" que contiene los datos de los entornos que terminaron.
++    if isinstance(info, dict) and "episode" in info:
++        # info["episode"] contiene un array de resultados para cada sub-entorno
++        for item in info["episode"]:
++            if item is not None:
++                print(f"episodic return {item['r']}")
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..eb4a63c 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.25.2"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_120843-tuj6f8rr/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch b/wandb/run-20260421_120843-tuj6f8rr/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
new file mode 100644
index 0000000000000000000000000000000000000000..e05b57f88a0bf01b22493c4f505a0543c4cf6fd1
--- /dev/null
+++ b/wandb/run-20260421_120843-tuj6f8rr/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
@@ -0,0 +1,1099 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..4c3cf56 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
++    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"},
++    {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"},
++]
++
++[[package]]
++name = "charset-normalizer"
++version = "3.4.7"
++description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[[package]]
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++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "eval_type_backport-0.3.1-py3-none-any.whl", hash = "sha256:279ab641905e9f11129f56a8a78f493518515b83402b860f6f06dd7c011fdfa8"},
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++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++smmap = ">=3.0.1,<6"
++
++[[package]]
++name = "gitpython"
++version = "3.1.46"
++description = "GitPython is a Python library used to interact with Git repositories"
++optional = false
++python-versions = ">=3.7"
++files = [
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++    {file = "gitpython-3.1.46.tar.gz", hash = "sha256:400124c7d0ef4ea03f7310ac2fbf7151e09ff97f2a3288d64a440c584a29c37f"},
++]
++
++[package.dependencies]
++gitdb = ">=4.0.1,<5"
++typing-extensions = {version = ">=3.10.0.2", markers = "python_version < \"3.10\""}
++
++[package.extras]
++doc = ["sphinx (>=7.1.2,<7.2)", "sphinx-autodoc-typehints", "sphinx_rtd_theme"]
++test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy (==1.18.2)", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
++
+ [[package]]
+ name = "grpcio"
+ version = "1.80.0"
+@@ -303,6 +524,45 @@ typing-extensions = ">=4.12,<5.0"
+ [package.extras]
+ protobuf = ["grpcio-tools (>=1.80.0)"]
+ 
++[[package]]
++name = "gym"
++version = "0.25.2"
++description = "Gym: A universal API for reinforcement learning environments"
++optional = false
++python-versions = ">=3.6"
++files = [
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++]
++
++[package.dependencies]
++cloudpickle = ">=1.2.0"
++gym_notices = ">=0.0.4"
++importlib_metadata = {version = ">=4.8.0", markers = "python_version < \"3.10\""}
++numpy = ">=1.18.0"
++
++[package.extras]
++accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"]
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++atari = ["ale-py (>=0.7.5,<0.8.0)"]
++box2d = ["box2d-py (==2.3.5)", "pygame (==2.1.0)", "swig (==4.*)"]
++classic-control = ["pygame (==2.1.0)"]
++mujoco = ["imageio (>=2.14.1)", "mujoco (==2.2.0)"]
++mujoco-py = ["mujoco_py (>=2.1,<2.2)"]
++other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "opencv-python (>=3.0)"]
++testing = ["box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "mujoco (==2.2.0)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)", "swig (==4.*)"]
++toy-text = ["pygame (==2.1.0)"]
++
++[[package]]
++name = "gym-notices"
++version = "0.1.0"
++description = "Notices for gym"
++optional = false
++python-versions = "*"
++files = [
++    {file = "gym_notices-0.1.0-py3-none-any.whl", hash = "sha256:a943af4446cb619d04fd1e470b9272b4473e08a06d1c7cc9005755a4a0b8c905"},
++    {file = "gym_notices-0.1.0.tar.gz", hash = "sha256:9f9477ef68a8c15e42625d4fa53631237e3e6ae947f325b5c149c081499adc1b"},
++]
++
+ [[package]]
+ name = "gymnasium"
+ version = "0.29.1"
+@@ -334,6 +594,36 @@ other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-pyt
+ testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
+ toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+ 
++[[package]]
++name = "idna"
++version = "3.11"
++description = "Internationalized Domain Names in Applications (IDNA)"
++optional = false
++python-versions = ">=3.8"
++files = [
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++    {file = "idna-3.11.tar.gz", hash = "sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902"},
++]
++
++[package.extras]
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++
++[[package]]
++name = "imageio-ffmpeg"
++version = "0.6.0"
++description = "FFMPEG wrapper for Python"
++optional = false
++python-versions = ">=3.9"
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+ 
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+ typing = ["typing-extensions"]
+ xmp = ["defusedxml"]
+ 
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++http2 = ["httpcore[http2] (==1.*)"]
++httpx = ["httpx (>=0.16.0)"]
++huey = ["huey (>=2)"]
++huggingface-hub = ["huggingface_hub (>=0.22)"]
++langchain = ["langchain (>=0.0.210)"]
++langgraph = ["langgraph (>=0.6.6)"]
++launchdarkly = ["launchdarkly-server-sdk (>=9.8.0)"]
++litellm = ["litellm (>=1.77.5,!=1.82.7,!=1.82.8)"]
++litestar = ["litestar (>=2.0.0)"]
++loguru = ["loguru (>=0.5)"]
++mcp = ["mcp (>=1.15.0)"]
++openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
++openfeature = ["openfeature-sdk (>=0.7.1)"]
++opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
++opentelemetry-experimental = ["opentelemetry-distro"]
++opentelemetry-otlp = ["opentelemetry-distro[otlp] (>=0.35b0)"]
++pure-eval = ["asttokens", "executing", "pure_eval"]
++pydantic-ai = ["pydantic-ai (>=1.0.0)"]
++pymongo = ["pymongo (>=3.1)"]
++pyspark = ["pyspark (>=2.4.4)"]
++quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
++rq = ["rq (>=0.6)"]
++sanic = ["sanic (>=0.8)"]
++sqlalchemy = ["sqlalchemy (>=1.2)"]
++starlette = ["starlette (>=0.19.1)"]
++starlite = ["starlite (>=1.48)"]
++statsig = ["statsig (>=0.55.3)"]
++tornado = ["tornado (>=6)"]
++unleash = ["UnleashClient (>=6.0.1)"]
++
++[[package]]
++name = "setuptools"
++version = "65.5.0"
++description = "Easily download, build, install, upgrade, and uninstall Python packages"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "setuptools-65.5.0-py3-none-any.whl", hash = "sha256:f62ea9da9ed6289bfe868cd6845968a2c854d1427f8548d52cae02a42b4f0356"},
++    {file = "setuptools-65.5.0.tar.gz", hash = "sha256:512e5536220e38146176efb833d4a62aa726b7bbff82cfbc8ba9eaa3996e0b17"},
++]
++
++[package.extras]
++docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
++testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
++testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
++
++[[package]]
++name = "smmap"
++version = "5.0.3"
++description = "A pure Python implementation of a sliding window memory map manager"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "smmap-5.0.3-py3-none-any.whl", hash = "sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f"},
++    {file = "smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c"},
++]
+ 
+ [[package]]
+ name = "sympy"
+@@ -1197,6 +1881,81 @@ files = [
+     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+ ]
+ 
++[[package]]
++name = "typing-inspection"
++version = "0.4.2"
++description = "Runtime typing introspection tools"
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
++    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
++]
++
++[package.dependencies]
++typing-extensions = ">=4.12.0"
++
++[[package]]
++name = "urllib3"
++version = "2.6.3"
++description = "HTTP library with thread-safe connection pooling, file post, and more."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
++brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"]
++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:1ece94a2a5eda1d0e3a2d8a2fd28aa0187705d6efa5ac4c0b8680083583b7ec1"},
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:92f6f303fe2af50e3f711833a835150f9b4d8082874bfd9868cf15491ea2947e"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:a17aae051a31831388cff880251c1b5bc38fbf6a283a0ee7c543709e8e9633d1"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:28a14ffc014e523498d077dfde12839b7be586ca8c3190e72e7167c1aea6ee4c"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1995,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "89750cd6ebb50c516550a1896508559bba968e0ca72314d4dfa46ba5469fa298"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..8837ac4 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -3,6 +3,10 @@ import os
+ from distutils.util import strtobool
+ import time
+ from torch.utils.tensorboard import SummaryWriter
++import torch
++import random
++import numpy as np
++import gym
+ 
+ def parse_args():
+     parser = argparse.ArgumentParser()
+@@ -16,21 +20,80 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
+     args = parser.parse_args()
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    env = gym.make("CartPole-v1")
++    env = gym.wrappers.RecordEpisodeStatistics(env)
++    env = gym.wrappers.RecordVideo(env, "videos", episode_trigger=lambda t: t % 100 == 0)
++    observation = env.reset()
++    episodic_return = 0
++    for _ in range(200):
++        action = env.action_space.sample()
++        observation, reward, done, info = env.step(action)
++        episodic_return += reward
++        if done:
++            observation = env.reset()
++            print(f"episodic return: {episodic_return}")
++            episodic_return = 0
++    env.close()
++
++    def make_env(gym_id):
++        def thunk():
++            env = gym.make(gym_id)
++            env = gym.wrappers.RecordEpisodeStatistics(env)
++            env = gym.wrappers.RecordVideo(env, "videos", episode_trigger=lambda t: t % 100 == 0)
++            return env
++        return thunk
++    
++envs = gym.vector.SyncVectorEnv([make_env(args.gym_id)])
++observation = envs.reset()
++for _ in range(200):
++    action = envs.action_space.sample()
++    observation, reward, done, info = envs.step(action)
++    
++    # En SyncVectorEnv, info es un diccionario. 
++    # Buscamos la clave "episode" que contiene los datos de los entornos que terminaron.
++    if isinstance(info, dict) and "episode" in info:
++        # info["episode"] contiene un array de resultados para cada sub-entorno
++        for item in info["episode"]:
++            if item is not None:
++                print(f"episodic return {item['r']}")
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..eb4a63c 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.25.2"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_120843-tuj6f8rr/files/output.log b/wandb/run-20260421_120843-tuj6f8rr/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..e680170db54ec6a7781448c24d10be3f13beb9e2
--- /dev/null
+++ b/wandb/run-20260421_120843-tuj6f8rr/files/output.log
@@ -0,0 +1,34 @@
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py:317: DeprecationWarning: WARN: Initializing wrapper in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.
+  deprecation(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/step_api_compatibility.py:39: DeprecationWarning: WARN: Initializing environment in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.
+  deprecation(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/record_video.py:78: UserWarning: WARN: Overwriting existing videos at /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/videos folder (try specifying a different `video_folder` for the `RecordVideo` wrapper if this is not desired)
+  logger.warn(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/monitoring/video_recorder.py:78: DeprecationWarning: WARN: Recording ability for environment CartPole-v1 initialized with `render_mode=None` is marked as deprecated and will be removed in the future.
+  logger.deprecation(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py:43: DeprecationWarning: WARN: The argument mode in render method is deprecated; use render_mode during environment initialization instead.
+See here for more information: https://www.gymlibrary.ml/content/api/
+  deprecation(
+episodic return: 21.0
+episodic return: 12.0
+episodic return: 19.0
+episodic return: 17.0
+episodic return: 25.0
+episodic return: 13.0
+episodic return: 22.0
+episodic return: 12.0
+episodic return: 14.0
+episodic return: 11.0
+episodic return: 10.0
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/vector/vector_env.py:56: DeprecationWarning: WARN: Initializing vector env in old step API which returns one bool array instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.
+  deprecation(
+episodic return 23.0
+episodic return 15.0
+episodic return 14.0
+episodic return 13.0
+episodic return 10.0
+episodic return 15.0
+episodic return 43.0
+episodic return 34.0
+episodic return 11.0
+episodic return 12.0
diff --git a/wandb/run-20260421_120843-tuj6f8rr/files/requirements.txt b/wandb/run-20260421_120843-tuj6f8rr/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3f020f4ecd61d8f6a97ef8435a2e387b50f5a3c3
--- /dev/null
+++ b/wandb/run-20260421_120843-tuj6f8rr/files/requirements.txt
@@ -0,0 +1,70 @@
+mpmath==1.3.0
+nvidia-nvtx-cu12==12.6.77
+pluggy==1.6.0
+charset-normalizer==3.4.7
+Markdown==3.9
+protobuf==6.33.6
+Werkzeug==3.1.8
+gym-notices==0.1.0
+tomli==2.4.1
+GitPython==3.1.46
+nvidia-cuda-cupti-cu12==12.6.80
+MarkupSafe==3.0.3
+certifi==2026.2.25
+nvidia-cublas-cu12==12.6.4.1
+nvidia-cusparselt-cu12==0.6.3
+networkx==3.2.1
+wandb==0.26.0
+tensorboard==2.20.0
+setuptools==65.5.0
+nvidia-cusparse-cu12==12.5.4.2
+gitdb==4.0.12
+pip==24.0
+Jinja2==3.1.6
+pydantic==2.13.3
+coverage==7.10.7
+sentry-sdk==2.58.0
+exceptiongroup==1.3.1
+sympy==1.14.0
+annotated-types==0.7.0
+absl-py==2.3.1
+smmap==5.0.3
+importlib_metadata==8.7.1
+idna==3.11
+pillow==11.3.0
+tensorboard-data-server==0.7.2
+cloudpickle==3.1.2
+platformdirs==4.4.0
+gym==0.25.2
+pytest==7.4.4
+nvidia-curand-cu12==10.3.7.77
+fsspec==2025.10.0
+click==8.1.8
+nvidia-cuda-nvrtc-cu12==12.6.77
+gymnasium==0.29.1
+numpy==1.23.5
+imageio-ffmpeg==0.6.0
+nvidia-nccl-cu12==2.26.2
+urllib3==2.6.3
+packaging==26.0
+zipp==3.23.1
+nvidia-cufile-cu12==1.11.1.6
+nvidia-cudnn-cu12==9.5.1.17
+triton==3.3.1
+filelock==3.19.1
+Farama-Notifications==0.0.4
+grpcio==1.80.0
+iniconfig==2.1.0
+typing_extensions==4.15.0
+PyYAML==6.0.3
+nvidia-cusolver-cu12==11.7.1.2
+nvidia-cuda-runtime-cu12==12.6.77
+pygame==2.6.1
+requests==2.32.5
+pydantic_core==2.46.3
+torch==2.7.1
+nvidia-cufft-cu12==11.3.0.4
+eval_type_backport==0.3.1
+wheel==0.38.4
+typing-inspection==0.4.2
+nvidia-nvjitlink-cu12==12.6.85
diff --git a/wandb/run-20260421_120843-tuj6f8rr/files/runs/CartPole-v1_ppo_1_1776766122/events.out.tfevents.1776766124.BL-LFERNANDEZ.local.eurecat.org.50518.0 b/wandb/run-20260421_120843-tuj6f8rr/files/runs/CartPole-v1_ppo_1_1776766122/events.out.tfevents.1776766124.BL-LFERNANDEZ.local.eurecat.org.50518.0
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+size 386
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new file mode 100644
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+++ b/wandb/run-20260421_120843-tuj6f8rr/files/wandb-metadata.json
@@ -0,0 +1,31 @@
+{
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+  "python": "CPython 3.9.5",
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+  "args": [
+    "--track"
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+  "git": {
+    "remote": "https://github.com/loisonchambers/Unit-8-RL-course.git",
+    "commit": "e1dc52c8934ce180fef18fbdf53a72281f134a52"
+  },
+  "email": "loisfernandezcmp@gmail.com",
+  "root": "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8",
+  "host": "BL-LFERNANDEZ.local.eurecat.org",
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new file mode 100644
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new file mode 100644
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+2026-04-21 12:08:43,143 INFO    MainThread:50518 [wandb_init.py:init():853] wandb.init called with sweep_config: {}
+config: {'exp_name': 'ppo', 'gym_id': 'CartPole-v1', 'learning_rate': 0.00025, 'seed': 1, 'total_timesteps': 25000, 'torch_deterministic': True, 'cuda': True, 'track': True, 'wandb_project_name': 'cleanRL', 'wandb_entity': None, '_wandb': {'code_path': 'code/ppo.py'}}
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diff --git a/wandb/run-20260421_120843-tuj6f8rr/run-tuj6f8rr.wandb b/wandb/run-20260421_120843-tuj6f8rr/run-tuj6f8rr.wandb
new file mode 100644
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diff --git a/wandb/run-20260421_121122-ps8vjlk9/files/code/ppo.py b/wandb/run-20260421_121122-ps8vjlk9/files/code/ppo.py
new file mode 100644
index 0000000000000000000000000000000000000000..509d6a62ad00c6b371aceb08131a749b85aa22be
--- /dev/null
+++ b/wandb/run-20260421_121122-ps8vjlk9/files/code/ppo.py
@@ -0,0 +1,101 @@
+import argparse
+import os
+from distutils.util import strtobool
+import time
+from torch.utils.tensorboard import SummaryWriter
+import torch
+import random
+import numpy as np
+import gym
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+        help='the name of this experiment')
+    parser.add_argument('--gym-id', type=str, default="CartPole-v1",
+        help='the id of the gym environment')
+    parser.add_argument('--learning-rate', type=float, default=2.5e-4,
+        help='the learning rate of the optimizer')
+    parser.add_argument('--seed', type=int, default=1,
+        help='seed of the experiment')
+    parser.add_argument('--total-timesteps', type=int, default=25000,
+        help='total timesteps of the experiments')
+    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+    parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, cuda will not be enabled by default')
+    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
+        help='if toggled, this experiment will be tracked with Weights and Biases')
+    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
+        help="the wandb's project name")
+    parser.add_argument('--wandb-entity', type=str, default=None,
+        help="the entity (team) of wandb's project")
+    args = parser.parse_args()
+    return args
+
+if __name__ == "__main__":
+    args = parse_args()
+    print(args)
+    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+    if args.track:
+        import wandb
+
+        wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            sync_tensorboard=True,
+            config=vars(args),
+            name=run_name,
+            monitor_gym=True,
+            save_code=True,
+        )
+    writer =SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
+    )
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
+
+    env = gym.make("CartPole-v1")
+    env = gym.wrappers.RecordEpisodeStatistics(env)
+    env = gym.wrappers.RecordVideo(env, "videos", episode_trigger=lambda t: t % 100 == 0)
+    observation = env.reset()
+    episodic_return = 0
+    for _ in range(200):
+        action = env.action_space.sample()
+        observation, reward, done, info = env.step(action)
+        episodic_return += reward
+        if done:
+            observation = env.reset()
+            print(f"episodic return: {episodic_return}")
+            episodic_return = 0
+    env.close()
+
+    def make_env(gym_id):
+        def thunk():
+            env = gym.make(gym_id)
+            env = gym.wrappers.RecordEpisodeStatistics(env)
+            env = gym.wrappers.RecordVideo(env, "videos", episode_trigger=lambda t: t % 100 == 0)
+            return env
+        return thunk
+    
+    envs = gym.vector.SyncVectorEnv([make_env(args.gym_id)])
+    observation = envs.reset()
+    for _ in range(200):
+        action = envs.action_space.sample()
+        observation, reward, done, info = envs.step(action)
+        
+        # En SyncVectorEnv, info es un diccionario. 
+        # Buscamos la clave "episode" que contiene los datos de los entornos que terminaron.
+        if isinstance(info, dict) and "episode" in info:
+            # info["episode"] contiene un array de resultados para cada sub-entorno
+            for item in info["episode"]:
+                if item is not None:
+                    print(f"episodic return {item['r']}")
+
+    
diff --git a/wandb/run-20260421_121122-ps8vjlk9/files/config.yaml b/wandb/run-20260421_121122-ps8vjlk9/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..4aeefaf4078221ce38bab28a595d5f91a0cf104f
--- /dev/null
+++ b/wandb/run-20260421_121122-ps8vjlk9/files/config.yaml
@@ -0,0 +1,65 @@
+_wandb:
+    value:
+        cli_version: 0.26.0
+        code_path: code/ppo.py
+        e:
+            x8v3s4hfotgxv0vr0mrw3tetqyr9tdn1:
+                args:
+                    - --track
+                codePath: ppo.py
+                codePathLocal: ppo.py
+                cpu_count: 12
+                cpu_count_logical: 14
+                disk:
+                    /:
+                        total: "499680116736"
+                        used: "141718966272"
+                email: loisfernandezcmp@gmail.com
+                executable: /home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/bin/python
+                git:
+                    commit: e1dc52c8934ce180fef18fbdf53a72281f134a52
+                    remote: https://github.com/loisonchambers/Unit-8-RL-course.git
+                host: BL-LFERNANDEZ.local.eurecat.org
+                memory:
+                    total: "33076023296"
+                os: Linux-6.17.0-20-generic-x86_64-with-glibc2.39
+                program: /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py
+                python: CPython 3.9.5
+                root: /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8
+                startedAt: "2026-04-21T10:11:22.241210Z"
+                writerId: x8v3s4hfotgxv0vr0mrw3tetqyr9tdn1
+        m: []
+        python_version: 3.9.5
+        t:
+            "1":
+                - 1
+            "2":
+                - 1
+            "3":
+                - 13
+                - 16
+                - 35
+            "4": 3.9.5
+            "5": 0.26.0
+            "12": 0.26.0
+            "13": linux-x86_64
+cuda:
+    value: true
+exp_name:
+    value: ppo
+gym_id:
+    value: CartPole-v1
+learning_rate:
+    value: 0.00025
+seed:
+    value: 1
+torch_deterministic:
+    value: true
+total_timesteps:
+    value: 25000
+track:
+    value: true
+wandb_entity:
+    value: null
+wandb_project_name:
+    value: cleanRL
diff --git a/wandb/run-20260421_121122-ps8vjlk9/files/diff.patch b/wandb/run-20260421_121122-ps8vjlk9/files/diff.patch
new file mode 100644
index 0000000000000000000000000000000000000000..59c411bda88e0e0412ce733bc0d574e092144dc3
--- /dev/null
+++ b/wandb/run-20260421_121122-ps8vjlk9/files/diff.patch
@@ -0,0 +1,1101 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..4c3cf56 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
++    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
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++]
++
++[[package]]
++name = "click"
++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2"},
++    {file = "click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a"},
++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "eval_type_backport-0.3.1-py3-none-any.whl", hash = "sha256:279ab641905e9f11129f56a8a78f493518515b83402b860f6f06dd7c011fdfa8"},
++    {file = "eval_type_backport-0.3.1.tar.gz", hash = "sha256:57e993f7b5b69d271e37482e62f74e76a0276c82490cf8e4f0dffeb6b332d5ed"},
++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
++optional = false
++python-versions = ">=3.7"
++files = [
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++    {file = "gitdb-4.0.12.tar.gz", hash = "sha256:5ef71f855d191a3326fcfbc0d5da835f26b13fbcba60c32c21091c349ffdb571"},
++]
++
++[package.dependencies]
++smmap = ">=3.0.1,<6"
++
++[[package]]
++name = "gitpython"
++version = "3.1.46"
++description = "GitPython is a Python library used to interact with Git repositories"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "gitpython-3.1.46-py3-none-any.whl", hash = "sha256:79812ed143d9d25b6d176a10bb511de0f9c67b1fa641d82097b0ab90398a2058"},
++    {file = "gitpython-3.1.46.tar.gz", hash = "sha256:400124c7d0ef4ea03f7310ac2fbf7151e09ff97f2a3288d64a440c584a29c37f"},
++]
++
++[package.dependencies]
++gitdb = ">=4.0.1,<5"
++typing-extensions = {version = ">=3.10.0.2", markers = "python_version < \"3.10\""}
++
++[package.extras]
++doc = ["sphinx (>=7.1.2,<7.2)", "sphinx-autodoc-typehints", "sphinx_rtd_theme"]
++test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy (==1.18.2)", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
++
+ [[package]]
+ name = "grpcio"
+ version = "1.80.0"
+@@ -303,6 +524,45 @@ typing-extensions = ">=4.12,<5.0"
+ [package.extras]
+ protobuf = ["grpcio-tools (>=1.80.0)"]
+ 
++[[package]]
++name = "gym"
++version = "0.25.2"
++description = "Gym: A universal API for reinforcement learning environments"
++optional = false
++python-versions = ">=3.6"
++files = [
++    {file = "gym-0.25.2.tar.gz", hash = "sha256:c8323f4c6f37363710b519742a0094316deddc9d8fc9e3f6deac08d6da47150c"},
++]
++
++[package.dependencies]
++cloudpickle = ">=1.2.0"
++gym_notices = ">=0.0.4"
++importlib_metadata = {version = ">=4.8.0", markers = "python_version < \"3.10\""}
++numpy = ">=1.18.0"
++
++[package.extras]
++accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"]
++all = ["ale-py (>=0.7.5,<0.8.0)", "box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "mujoco (==2.2.0)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)", "swig (==4.*)"]
++atari = ["ale-py (>=0.7.5,<0.8.0)"]
++box2d = ["box2d-py (==2.3.5)", "pygame (==2.1.0)", "swig (==4.*)"]
++classic-control = ["pygame (==2.1.0)"]
++mujoco = ["imageio (>=2.14.1)", "mujoco (==2.2.0)"]
++mujoco-py = ["mujoco_py (>=2.1,<2.2)"]
++other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "opencv-python (>=3.0)"]
++testing = ["box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "mujoco (==2.2.0)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)", "swig (==4.*)"]
++toy-text = ["pygame (==2.1.0)"]
++
++[[package]]
++name = "gym-notices"
++version = "0.1.0"
++description = "Notices for gym"
++optional = false
++python-versions = "*"
++files = [
++    {file = "gym_notices-0.1.0-py3-none-any.whl", hash = "sha256:a943af4446cb619d04fd1e470b9272b4473e08a06d1c7cc9005755a4a0b8c905"},
++    {file = "gym_notices-0.1.0.tar.gz", hash = "sha256:9f9477ef68a8c15e42625d4fa53631237e3e6ae947f325b5c149c081499adc1b"},
++]
++
+ [[package]]
+ name = "gymnasium"
+ version = "0.29.1"
+@@ -334,6 +594,36 @@ other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-pyt
+ testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
+ toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+ 
++[[package]]
++name = "idna"
++version = "3.11"
++description = "Internationalized Domain Names in Applications (IDNA)"
++optional = false
++python-versions = ">=3.8"
++files = [
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++    {file = "idna-3.11.tar.gz", hash = "sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902"},
++]
++
++[package.extras]
++all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
++
++[[package]]
++name = "imageio-ffmpeg"
++version = "0.6.0"
++description = "FFMPEG wrapper for Python"
++optional = false
++python-versions = ">=3.9"
++files = [
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++    {file = "imageio_ffmpeg-0.6.0.tar.gz", hash = "sha256:e2556bed8e005564a9f925bb7afa4002d82770d6b08825078b7697ab88ba1755"},
++]
++
+ [[package]]
+ name = "importlib-metadata"
+ version = "8.7.1"
+@@ -538,56 +828,39 @@ test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
+ 
+ [[package]]
+ name = "numpy"
+-version = "2.0.2"
+-description = "Fundamental package for array computing in Python"
++version = "1.23.5"
++description = "NumPy is the fundamental package for array computing with Python."
+ optional = false
+-python-versions = ">=3.9"
++python-versions = ">=3.8"
+ files = [
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++unleash = ["UnleashClient (>=6.0.1)"]
++
++[[package]]
++name = "setuptools"
++version = "65.5.0"
++description = "Easily download, build, install, upgrade, and uninstall Python packages"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "setuptools-65.5.0-py3-none-any.whl", hash = "sha256:f62ea9da9ed6289bfe868cd6845968a2c854d1427f8548d52cae02a42b4f0356"},
++    {file = "setuptools-65.5.0.tar.gz", hash = "sha256:512e5536220e38146176efb833d4a62aa726b7bbff82cfbc8ba9eaa3996e0b17"},
++]
++
++[package.extras]
++docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
++testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
++testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
++
++[[package]]
++name = "smmap"
++version = "5.0.3"
++description = "A pure Python implementation of a sliding window memory map manager"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "smmap-5.0.3-py3-none-any.whl", hash = "sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f"},
++    {file = "smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c"},
++]
+ 
+ [[package]]
+ name = "sympy"
+@@ -1197,6 +1881,81 @@ files = [
+     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+ ]
+ 
++[[package]]
++name = "typing-inspection"
++version = "0.4.2"
++description = "Runtime typing introspection tools"
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
++    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
++]
++
++[package.dependencies]
++typing-extensions = ">=4.12.0"
++
++[[package]]
++name = "urllib3"
++version = "2.6.3"
++description = "HTTP library with thread-safe connection pooling, file post, and more."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
++brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"]
++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:1ece94a2a5eda1d0e3a2d8a2fd28aa0187705d6efa5ac4c0b8680083583b7ec1"},
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:92f6f303fe2af50e3f711833a835150f9b4d8082874bfd9868cf15491ea2947e"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:a17aae051a31831388cff880251c1b5bc38fbf6a283a0ee7c543709e8e9633d1"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:28a14ffc014e523498d077dfde12839b7be586ca8c3190e72e7167c1aea6ee4c"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1995,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "89750cd6ebb50c516550a1896508559bba968e0ca72314d4dfa46ba5469fa298"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..509d6a6 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -3,6 +3,10 @@ import os
+ from distutils.util import strtobool
+ import time
+ from torch.utils.tensorboard import SummaryWriter
++import torch
++import random
++import numpy as np
++import gym
+ 
+ def parse_args():
+     parser = argparse.ArgumentParser()
+@@ -16,21 +20,82 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
+     args = parser.parse_args()
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    env = gym.make("CartPole-v1")
++    env = gym.wrappers.RecordEpisodeStatistics(env)
++    env = gym.wrappers.RecordVideo(env, "videos", episode_trigger=lambda t: t % 100 == 0)
++    observation = env.reset()
++    episodic_return = 0
++    for _ in range(200):
++        action = env.action_space.sample()
++        observation, reward, done, info = env.step(action)
++        episodic_return += reward
++        if done:
++            observation = env.reset()
++            print(f"episodic return: {episodic_return}")
++            episodic_return = 0
++    env.close()
++
++    def make_env(gym_id):
++        def thunk():
++            env = gym.make(gym_id)
++            env = gym.wrappers.RecordEpisodeStatistics(env)
++            env = gym.wrappers.RecordVideo(env, "videos", episode_trigger=lambda t: t % 100 == 0)
++            return env
++        return thunk
++    
++    envs = gym.vector.SyncVectorEnv([make_env(args.gym_id)])
++    observation = envs.reset()
++    for _ in range(200):
++        action = envs.action_space.sample()
++        observation, reward, done, info = envs.step(action)
++        
++        # En SyncVectorEnv, info es un diccionario. 
++        # Buscamos la clave "episode" que contiene los datos de los entornos que terminaron.
++        if isinstance(info, dict) and "episode" in info:
++            # info["episode"] contiene un array de resultados para cada sub-entorno
++            for item in info["episode"]:
++                if item is not None:
++                    print(f"episodic return {item['r']}")
++
++    
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..eb4a63c 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.25.2"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_121122-ps8vjlk9/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch b/wandb/run-20260421_121122-ps8vjlk9/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
new file mode 100644
index 0000000000000000000000000000000000000000..59c411bda88e0e0412ce733bc0d574e092144dc3
--- /dev/null
+++ b/wandb/run-20260421_121122-ps8vjlk9/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
@@ -0,0 +1,1101 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..4c3cf56 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
++    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"},
++    {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"},
++]
++
++[[package]]
++name = "charset-normalizer"
++version = "3.4.7"
++description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "charset_normalizer-3.4.7-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:cdd68a1fb318e290a2077696b7eb7a21a49163c455979c639bf5a5dcdc46617d"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e17b8d5d6a8c47c85e68ca8379def1303fd360c3e22093a807cd34a71cd082b8"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:511ef87c8aec0783e08ac18565a16d435372bc1ac25a91e6ac7f5ef2b0bff790"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:007d05ec7321d12a40227aae9e2bc6dca73f3cb21058999a1df9e193555a9dcc"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:cf29836da5119f3c8a8a70667b0ef5fdca3bb12f80fd06487cfa575b3909b393"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux_2_31_armv7l.whl", hash = "sha256:12d8baf840cc7889b37c7c770f478adea7adce3dcb3944d02ec87508e2dcf153"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:d560742f3c0d62afaccf9f41fe485ed69bd7661a241f86a3ef0f0fb8b1a397af"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:b14b2d9dac08e28bb8046a1a0434b1750eb221c8f5b87a68f4fa11a6f97b5e34"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_armv7l.whl", hash = "sha256:bc17a677b21b3502a21f66a8cc64f5bfad4df8a0b8434d661666f8ce90ac3af1"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:750e02e074872a3fad7f233b47734166440af3cdea0add3e95163110816d6752"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_riscv64.whl", hash = "sha256:4e5163c14bffd570ef2affbfdd77bba66383890797df43dc8b4cc7d6f500bf53"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:6ed74185b2db44f41ef35fd1617c5888e59792da9bbc9190d6c7300617182616"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:94e1885b270625a9a828c9793b4d52a64445299baa1fea5a173bf1d3dd9a1a5a"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-win32.whl", hash = "sha256:6785f414ae0f3c733c437e0f3929197934f526d19dfaa75e18fdb4f94c6fb374"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-win_amd64.whl", hash = "sha256:6696b7688f54f5af4462118f0bfa7c1621eeb87154f77fa04b9295ce7a8f2943"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-win_arm64.whl", hash = "sha256:66671f93accb62ed07da56613636f3641f1a12c13046ce91ffc923721f23c008"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:7641bb8895e77f921102f72833904dcd9901df5d6d72a2ab8f31d04b7e51e4e7"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:202389074300232baeb53ae2569a60901f7efadd4245cf3a3bf0617d60b439d7"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:30b8d1d8c52a48c2c5690e152c169b673487a2a58de1ec7393196753063fcd5e"},
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++
++[[package]]
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++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "eval_type_backport-0.3.1-py3-none-any.whl", hash = "sha256:279ab641905e9f11129f56a8a78f493518515b83402b860f6f06dd7c011fdfa8"},
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++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++smmap = ">=3.0.1,<6"
++
++[[package]]
++name = "gitpython"
++version = "3.1.46"
++description = "GitPython is a Python library used to interact with Git repositories"
++optional = false
++python-versions = ">=3.7"
++files = [
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++
++[package.dependencies]
++gitdb = ">=4.0.1,<5"
++typing-extensions = {version = ">=3.10.0.2", markers = "python_version < \"3.10\""}
++
++[package.extras]
++doc = ["sphinx (>=7.1.2,<7.2)", "sphinx-autodoc-typehints", "sphinx_rtd_theme"]
++test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy (==1.18.2)", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
++
+ [[package]]
+ name = "grpcio"
+ version = "1.80.0"
+@@ -303,6 +524,45 @@ typing-extensions = ">=4.12,<5.0"
+ [package.extras]
+ protobuf = ["grpcio-tools (>=1.80.0)"]
+ 
++[[package]]
++name = "gym"
++version = "0.25.2"
++description = "Gym: A universal API for reinforcement learning environments"
++optional = false
++python-versions = ">=3.6"
++files = [
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++]
++
++[package.dependencies]
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++gym_notices = ">=0.0.4"
++importlib_metadata = {version = ">=4.8.0", markers = "python_version < \"3.10\""}
++numpy = ">=1.18.0"
++
++[package.extras]
++accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"]
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++mujoco-py = ["mujoco_py (>=2.1,<2.2)"]
++other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "opencv-python (>=3.0)"]
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++toy-text = ["pygame (==2.1.0)"]
++
++[[package]]
++name = "gym-notices"
++version = "0.1.0"
++description = "Notices for gym"
++optional = false
++python-versions = "*"
++files = [
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++]
++
+ [[package]]
+ name = "gymnasium"
+ version = "0.29.1"
+@@ -334,6 +594,36 @@ other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-pyt
+ testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
+ toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+ 
++[[package]]
++name = "idna"
++version = "3.11"
++description = "Internationalized Domain Names in Applications (IDNA)"
++optional = false
++python-versions = ">=3.8"
++files = [
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++
++[package.extras]
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++
++[[package]]
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++
+ [[package]]
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+ version = "8.7.1"
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+ 
+ [[package]]
+ name = "numpy"
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++version = "1.23.5"
++description = "NumPy is the fundamental package for array computing with Python."
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++python-versions = ">=3.8"
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++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1995,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "89750cd6ebb50c516550a1896508559bba968e0ca72314d4dfa46ba5469fa298"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..509d6a6 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -3,6 +3,10 @@ import os
+ from distutils.util import strtobool
+ import time
+ from torch.utils.tensorboard import SummaryWriter
++import torch
++import random
++import numpy as np
++import gym
+ 
+ def parse_args():
+     parser = argparse.ArgumentParser()
+@@ -16,21 +20,82 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
+     args = parser.parse_args()
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    env = gym.make("CartPole-v1")
++    env = gym.wrappers.RecordEpisodeStatistics(env)
++    env = gym.wrappers.RecordVideo(env, "videos", episode_trigger=lambda t: t % 100 == 0)
++    observation = env.reset()
++    episodic_return = 0
++    for _ in range(200):
++        action = env.action_space.sample()
++        observation, reward, done, info = env.step(action)
++        episodic_return += reward
++        if done:
++            observation = env.reset()
++            print(f"episodic return: {episodic_return}")
++            episodic_return = 0
++    env.close()
++
++    def make_env(gym_id):
++        def thunk():
++            env = gym.make(gym_id)
++            env = gym.wrappers.RecordEpisodeStatistics(env)
++            env = gym.wrappers.RecordVideo(env, "videos", episode_trigger=lambda t: t % 100 == 0)
++            return env
++        return thunk
++    
++    envs = gym.vector.SyncVectorEnv([make_env(args.gym_id)])
++    observation = envs.reset()
++    for _ in range(200):
++        action = envs.action_space.sample()
++        observation, reward, done, info = envs.step(action)
++        
++        # En SyncVectorEnv, info es un diccionario. 
++        # Buscamos la clave "episode" que contiene los datos de los entornos que terminaron.
++        if isinstance(info, dict) and "episode" in info:
++            # info["episode"] contiene un array de resultados para cada sub-entorno
++            for item in info["episode"]:
++                if item is not None:
++                    print(f"episodic return {item['r']}")
++
++    
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..eb4a63c 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.25.2"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_121122-ps8vjlk9/files/output.log b/wandb/run-20260421_121122-ps8vjlk9/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..302d54db54cd11dfaffa7fc00c2865b8f59323b5
--- /dev/null
+++ b/wandb/run-20260421_121122-ps8vjlk9/files/output.log
@@ -0,0 +1,33 @@
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py:317: DeprecationWarning: WARN: Initializing wrapper in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.
+  deprecation(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/step_api_compatibility.py:39: DeprecationWarning: WARN: Initializing environment in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.
+  deprecation(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/record_video.py:78: UserWarning: WARN: Overwriting existing videos at /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/videos folder (try specifying a different `video_folder` for the `RecordVideo` wrapper if this is not desired)
+  logger.warn(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/monitoring/video_recorder.py:78: DeprecationWarning: WARN: Recording ability for environment CartPole-v1 initialized with `render_mode=None` is marked as deprecated and will be removed in the future.
+  logger.deprecation(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py:43: DeprecationWarning: WARN: The argument mode in render method is deprecated; use render_mode during environment initialization instead.
+See here for more information: https://www.gymlibrary.ml/content/api/
+  deprecation(
+episodic return: 11.0
+episodic return: 26.0
+episodic return: 14.0
+episodic return: 12.0
+episodic return: 29.0
+episodic return: 23.0
+episodic return: 32.0
+episodic return: 24.0
+episodic return: 9.0
+episodic return: 14.0
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/vector/vector_env.py:56: DeprecationWarning: WARN: Initializing vector env in old step API which returns one bool array instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.
+  deprecation(
+episodic return 35.0
+episodic return 15.0
+episodic return 27.0
+episodic return 11.0
+episodic return 13.0
+episodic return 11.0
+episodic return 27.0
+episodic return 16.0
+episodic return 27.0
+episodic return 14.0
diff --git a/wandb/run-20260421_121122-ps8vjlk9/files/requirements.txt b/wandb/run-20260421_121122-ps8vjlk9/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3f020f4ecd61d8f6a97ef8435a2e387b50f5a3c3
--- /dev/null
+++ b/wandb/run-20260421_121122-ps8vjlk9/files/requirements.txt
@@ -0,0 +1,70 @@
+mpmath==1.3.0
+nvidia-nvtx-cu12==12.6.77
+pluggy==1.6.0
+charset-normalizer==3.4.7
+Markdown==3.9
+protobuf==6.33.6
+Werkzeug==3.1.8
+gym-notices==0.1.0
+tomli==2.4.1
+GitPython==3.1.46
+nvidia-cuda-cupti-cu12==12.6.80
+MarkupSafe==3.0.3
+certifi==2026.2.25
+nvidia-cublas-cu12==12.6.4.1
+nvidia-cusparselt-cu12==0.6.3
+networkx==3.2.1
+wandb==0.26.0
+tensorboard==2.20.0
+setuptools==65.5.0
+nvidia-cusparse-cu12==12.5.4.2
+gitdb==4.0.12
+pip==24.0
+Jinja2==3.1.6
+pydantic==2.13.3
+coverage==7.10.7
+sentry-sdk==2.58.0
+exceptiongroup==1.3.1
+sympy==1.14.0
+annotated-types==0.7.0
+absl-py==2.3.1
+smmap==5.0.3
+importlib_metadata==8.7.1
+idna==3.11
+pillow==11.3.0
+tensorboard-data-server==0.7.2
+cloudpickle==3.1.2
+platformdirs==4.4.0
+gym==0.25.2
+pytest==7.4.4
+nvidia-curand-cu12==10.3.7.77
+fsspec==2025.10.0
+click==8.1.8
+nvidia-cuda-nvrtc-cu12==12.6.77
+gymnasium==0.29.1
+numpy==1.23.5
+imageio-ffmpeg==0.6.0
+nvidia-nccl-cu12==2.26.2
+urllib3==2.6.3
+packaging==26.0
+zipp==3.23.1
+nvidia-cufile-cu12==1.11.1.6
+nvidia-cudnn-cu12==9.5.1.17
+triton==3.3.1
+filelock==3.19.1
+Farama-Notifications==0.0.4
+grpcio==1.80.0
+iniconfig==2.1.0
+typing_extensions==4.15.0
+PyYAML==6.0.3
+nvidia-cusolver-cu12==11.7.1.2
+nvidia-cuda-runtime-cu12==12.6.77
+pygame==2.6.1
+requests==2.32.5
+pydantic_core==2.46.3
+torch==2.7.1
+nvidia-cufft-cu12==11.3.0.4
+eval_type_backport==0.3.1
+wheel==0.38.4
+typing-inspection==0.4.2
+nvidia-nvjitlink-cu12==12.6.85
diff --git a/wandb/run-20260421_121122-ps8vjlk9/files/runs/CartPole-v1_ppo_1_1776766281/events.out.tfevents.1776766283.BL-LFERNANDEZ.local.eurecat.org.51127.0 b/wandb/run-20260421_121122-ps8vjlk9/files/runs/CartPole-v1_ppo_1_1776766281/events.out.tfevents.1776766283.BL-LFERNANDEZ.local.eurecat.org.51127.0
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,3 @@
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+oid sha256:618af812c17147f953e1e421178ad38fb7c7d50e52e322e77c8e3dd9cec8ce20
+size 386
diff --git a/wandb/run-20260421_121122-ps8vjlk9/files/wandb-metadata.json b/wandb/run-20260421_121122-ps8vjlk9/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..9e45029acc963f6a6149dbbc55f5ca082b976a25
--- /dev/null
+++ b/wandb/run-20260421_121122-ps8vjlk9/files/wandb-metadata.json
@@ -0,0 +1,31 @@
+{
+  "os": "Linux-6.17.0-20-generic-x86_64-with-glibc2.39",
+  "python": "CPython 3.9.5",
+  "startedAt": "2026-04-21T10:11:22.241210Z",
+  "args": [
+    "--track"
+  ],
+  "program": "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py",
+  "codePath": "ppo.py",
+  "codePathLocal": "ppo.py",
+  "git": {
+    "remote": "https://github.com/loisonchambers/Unit-8-RL-course.git",
+    "commit": "e1dc52c8934ce180fef18fbdf53a72281f134a52"
+  },
+  "email": "loisfernandezcmp@gmail.com",
+  "root": "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8",
+  "host": "BL-LFERNANDEZ.local.eurecat.org",
+  "executable": "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/bin/python",
+  "cpu_count": 12,
+  "cpu_count_logical": 14,
+  "disk": {
+    "/": {
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+    }
+  },
+  "memory": {
+    "total": "33076023296"
+  },
+  "writerId": "x8v3s4hfotgxv0vr0mrw3tetqyr9tdn1"
+}
\ No newline at end of file
diff --git a/wandb/run-20260421_121122-ps8vjlk9/files/wandb-summary.json b/wandb/run-20260421_121122-ps8vjlk9/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..3e934fbe66c63b349c5526b2b80c1038f688ef08
--- /dev/null
+++ b/wandb/run-20260421_121122-ps8vjlk9/files/wandb-summary.json
@@ -0,0 +1 @@
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diff --git a/wandb/run-20260421_121122-ps8vjlk9/logs/debug-core.log b/wandb/run-20260421_121122-ps8vjlk9/logs/debug-core.log
new file mode 100644
index 0000000000000000000000000000000000000000..ba8ebad54e82b1f4316b32e5efdc9e71115d6b19
--- /dev/null
+++ b/wandb/run-20260421_121122-ps8vjlk9/logs/debug-core.log
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diff --git a/wandb/run-20260421_121122-ps8vjlk9/logs/debug-internal.log b/wandb/run-20260421_121122-ps8vjlk9/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..3d8a8dfb944537387c36402cff37b1bfb40c0e3f
--- /dev/null
+++ b/wandb/run-20260421_121122-ps8vjlk9/logs/debug-internal.log
@@ -0,0 +1,19 @@
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+{"time":"2026-04-21T12:11:22.85416771+02:00","level":"INFO","msg":"writer: started","stream_id":"ps8vjlk9"}
+{"time":"2026-04-21T12:11:23.933552522+02:00","level":"INFO","msg":"filestream: sending request","total_files":1,"console_offset":0,"console_lines":3}
+{"time":"2026-04-21T12:11:24.226991669+02:00","level":"INFO","msg":"stream: closing"}
+{"time":"2026-04-21T12:11:24.236524621+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
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+{"time":"2026-04-21T12:11:33.926761625+02:00","level":"INFO","msg":"tensorboard: saving file","fileLocation":"/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/runs/CartPole-v1_ppo_1_1776766281/events.out.tfevents.1776766283.BL-LFERNANDEZ.local.eurecat.org.51127.0","runPath":"runs/CartPole-v1_ppo_1_1776766281/events.out.tfevents.1776766283.BL-LFERNANDEZ.local.eurecat.org.51127.0"}
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+{"time":"2026-04-21T12:11:34.80836524+02:00","level":"INFO","msg":"filestream: sending request","total_files":2,"console_offset":2,"console_lines":31,"uploaded_len":9,"complete":true,"exit_code":0}
+{"time":"2026-04-21T12:11:35.073491715+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
+{"time":"2026-04-21T12:11:35.073723154+02:00","level":"INFO","msg":"handler: closed"}
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+{"time":"2026-04-21T12:11:35.073824723+02:00","level":"INFO","msg":"stream: closed"}
diff --git a/wandb/run-20260421_121122-ps8vjlk9/logs/debug.log b/wandb/run-20260421_121122-ps8vjlk9/logs/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..d2ce228c7a086fcacc63115bad7a7be394eefec4
--- /dev/null
+++ b/wandb/run-20260421_121122-ps8vjlk9/logs/debug.log
@@ -0,0 +1,22 @@
+2026-04-21 12:11:22,244 INFO    MainThread:51127 [wandb_setup.py:_flush():81] Current SDK version is 0.26.0
+2026-04-21 12:11:22,244 INFO    MainThread:51127 [wandb_setup.py:_flush():81] Configure stats pid to 51127
+2026-04-21 12:11:22,244 INFO    MainThread:51127 [wandb_setup.py:_flush():81] Loading settings from environment variables
+2026-04-21 12:11:22,244 INFO    MainThread:51127 [wandb_init.py:setup_run_log_directory():721] Logging user logs to /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/wandb/run-20260421_121122-ps8vjlk9/logs/debug.log
+2026-04-21 12:11:22,244 INFO    MainThread:51127 [wandb_init.py:setup_run_log_directory():722] Logging internal logs to /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/wandb/run-20260421_121122-ps8vjlk9/logs/debug-internal.log
+2026-04-21 12:11:22,268 INFO    MainThread:51127 [wandb_init.py:init():848] calling init triggers
+2026-04-21 12:11:22,268 INFO    MainThread:51127 [wandb_init.py:init():853] wandb.init called with sweep_config: {}
+config: {'exp_name': 'ppo', 'gym_id': 'CartPole-v1', 'learning_rate': 0.00025, 'seed': 1, 'total_timesteps': 25000, 'torch_deterministic': True, 'cuda': True, 'track': True, 'wandb_project_name': 'cleanRL', 'wandb_entity': None, '_wandb': {'code_path': 'code/ppo.py'}}
+2026-04-21 12:11:22,268 INFO    MainThread:51127 [wandb_init.py:init():896] starting backend
+2026-04-21 12:11:22,499 INFO    MainThread:51127 [wandb_init.py:init():911] sending inform_init request
+2026-04-21 12:11:22,516 INFO    MainThread:51127 [wandb_init.py:init():919] backend started and connected
+2026-04-21 12:11:22,527 INFO    MainThread:51127 [wandb_init.py:init():989] updated telemetry
+2026-04-21 12:11:22,546 INFO    MainThread:51127 [wandb_init.py:init():1013] communicating run to backend with 90.0 second timeout
+2026-04-21 12:11:23,879 INFO    MainThread:51127 [wandb_init.py:init():1058] starting run threads in backend
+2026-04-21 12:11:23,922 INFO    MainThread:51127 [wandb_run.py:_console_start():2542] atexit reg
+2026-04-21 12:11:23,922 INFO    MainThread:51127 [wandb_run.py:_redirect():2391] redirect: wrap_raw
+2026-04-21 12:11:23,922 INFO    MainThread:51127 [wandb_run.py:_redirect():2460] Wrapping output streams.
+2026-04-21 12:11:23,922 INFO    MainThread:51127 [wandb_run.py:_redirect():2483] Redirects installed.
+2026-04-21 12:11:23,925 INFO    MainThread:51127 [wandb_init.py:init():1098] run started, returning control to user process
+2026-04-21 12:11:23,925 INFO    MainThread:51127 [wandb_run.py:_tensorboard_callback():1632] tensorboard callback: runs/CartPole-v1_ppo_1_1776766281, True
+2026-04-21 12:11:24,227 INFO    wandb-AsyncioManager-main:51127 [service_client.py:_forward_responses():134] Reached EOF.
+2026-04-21 12:11:24,227 INFO    wandb-AsyncioManager-main:51127 [mailbox.py:close():155] Closing mailbox, abandoning 1 handles.
diff --git a/wandb/run-20260421_121122-ps8vjlk9/run-ps8vjlk9.wandb b/wandb/run-20260421_121122-ps8vjlk9/run-ps8vjlk9.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..ffc2703a5d92e0a61a2beb245e4ca54fccaadfa0
Binary files /dev/null and b/wandb/run-20260421_121122-ps8vjlk9/run-ps8vjlk9.wandb differ
diff --git a/wandb/run-20260421_155431-m5qntrul/files/code/ppo.py b/wandb/run-20260421_155431-m5qntrul/files/code/ppo.py
new file mode 100644
index 0000000000000000000000000000000000000000..29673f194c1e81ddfa6bf33c96f6cfd822f8473a
--- /dev/null
+++ b/wandb/run-20260421_155431-m5qntrul/files/code/ppo.py
@@ -0,0 +1,313 @@
+import argparse
+import os
+from distutils.util import strtobool
+import time
+import random
+import numpy as np
+import gym
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.distributions.categorical import Categorical
+from torch.utils.tensorboard import SummaryWriter
+
+def make_env(gym_id, seed, idx, capture_video, run_name):
+    def thunk():
+        env = gym.make(gym_id)
+        env = gym.wrappers.RecordEpisodeStatistics(env)
+        if capture_video:
+            if idx == 0:
+                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", record_video_trigger=lambda t: t % 1000 == 0)
+        env.seed(seed)
+        env.action_space.seed(seed)
+        env.observation_space.seed(seed)
+        return env
+    return thunk
+
+def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
+    torch.nn.init.orthogonal_(layer.weight, std)
+    torch.nn.init.constant_(layer.bias, bias_const)
+    return layer
+
+class Agent(nn.Module):
+    def __init__(self, envs):
+        super(Agent, self).__init__()
+        self.critic = nn.Sequential(
+            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,1), std=1.),
+        )
+
+        self.actor = nn.Sequential(
+            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
+        )
+
+    def get_value(self, x):
+        return self.critic(x)
+    
+    def get_action_and_value(self, x, action=None):
+        logits = self.actor(x)
+        probs = Categorical(logits=logits)
+        if action is None:
+            action = probs.sample()
+        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+        help='the name of this experiment')
+    parser.add_argument('--gym-id', type=str, default="CartPole-v1",
+        help='the id of the gym environment')
+    parser.add_argument('--learning-rate', type=float, default=2.5e-4,
+        help='the learning rate of the optimizer')
+    parser.add_argument('--seed', type=int, default=1,
+        help='seed of the experiment')
+    parser.add_argument('--total-timesteps', type=int, default=25000,
+        help='total timesteps of the experiments')
+    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+    parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, cuda will not be enabled by default')
+    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
+        help='if toggled, this experiment will be tracked with Weights and Biases')
+    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
+        help="the wandb's project name")
+    parser.add_argument('--wandb-entity', type=str, default=None,
+        help="the entity (team) of wandb's project")
+    parser.add_argument("--num-envs", type=int, default=4,
+    help="the number of parallel game environments")
+    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="whether to capture videos of the agent performances (check out `videos` folder)")
+    parser.add_argument("--num-steps", type=int, default=128,
+        help="the number of steps to run in each environment per policy rollout")
+    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggle learning rate annealing for policy and value networks")  
+    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Use GAE for advantage computation")  
+    parser.add_argument('--gamma', type=float, default=0.99,
+        help='the discount factor gamma')
+    parser.add_argument('--gae-lambda', type=float, default=0.95,
+        help='the lambda for the general advantage estimation')
+    parser.add_argument("--num-minibatches", type=int, default=4,
+        help="the number of mini-batches")
+    parser.add_argument("--update-epochs", type=int, default=4,
+        help="the K epochs to update the policy")
+    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggles advantages normalization")
+    parser.add_argument('--clip-coef', type=float, default=0.2,
+        help='the surrogate clipping coefficient')     
+    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
+    parser.add_argument('--ent-coef', type=float, default=0.01,
+        help='coefficient of the entropy')
+    parser.add_argument('--vf-coef', type=float, default=0.5,
+        help='coefficient of the value function')
+    parser.add_argument('--max-grad-norm', type=float, default=0.5,
+        help='the maximum norm for the gradient clipping')
+    parser.add_argument('--target-kl', type=float, default=None,
+        help='the target KL divergence threshold')
+    args = parser.parse_args()
+    args.batch_size = int(args.num_envs * args.num_steps)
+    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+    return args
+
+if __name__ == "__main__":
+    args = parse_args()
+    print(args)
+    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+    if args.track:
+        import wandb
+
+        wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            sync_tensorboard=True,
+            config=vars(args),
+            name=run_name,
+            monitor_gym=True,
+            save_code=True,
+        )
+    writer =SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
+    )
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
+
+    envs = gym.vector.SyncVectorEnv(
+        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
+    for i in range(args.num_envs)])
+    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
+
+    agent = Agent(envs).to(device)
+    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
+    
+    
+    # ALGO Logic: Storage setup
+    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
+    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
+    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
+
+    #TRY NOT TO MODIFY: start the game
+    global_step = 0
+    start_time =time.time()
+    next_obs = torch.Tensor(envs.reset()).to(device)
+    next_done = torch.zeros(args.num_envs).to(device)
+    num_updates = args.total_timesteps // args.batch_size
+
+    for update in range(1, num_updates+1):
+        # Annealing the rate if instructed to do so.abs
+        if args.anneal_lr:
+            frac = 1.0 -(update -1.0) / num_updates
+            lrnow = frac * args.learning_rate
+            optimizer.param_groups[0]["lr"]=lrnow
+        
+        for step in range(0, args.num_steps):
+            global_step += 1 * args.num_envs
+            obs[step] = next_obs
+            dones[step] = next_done
+
+            # ALGO LOGIC : action logic
+            with torch.no_grad():
+                action, logprob, _, value = agent.get_action_and_value(next_obs)
+                values[step] = value.flatten()
+            actions[step] = action
+            logprobs[step] = logprob
+
+            # TRY NOT TO MODIFY: execute the game and log data.abs
+            next_obs, reward, done, info = envs.step(action.cpu().numpy())
+            rewards[step] = torch.tensor(reward).to(device).view(-1)
+            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
+        
+            if isinstance(info, dict) and "episode" in info:
+                for item in info["episode"]:
+                    if item is not None:
+                        print(f"global_step={global_step}, episodic_return={item['r']}")
+                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
+                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
+                        break
+            
+            #bootstrap reward if not done
+            with torch.no_grad():
+                next_value = agent.get_value(next_obs).reshape(1,-1)
+                if args.gae:
+                    advantages = torch.zeros_like(rewards).to(device)
+                    lastgaelam = 0
+                    for t in reversed(range(args.num_steps)):
+                        if t == args.num_steps - 1:
+                            nextnonterminal = 1.0 - next_done
+                            nextvalues = next_value
+                        else:
+                            nextnonterminal = 1.0 - dones[t+1]
+                            nextvalues = values[t+1]
+                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
+                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
+                    returns =advantages + values
+                else:
+                    returns =torch.zeros_like(rewards).to(device)
+                    for t in reversed(range(args.num_steps)):
+                        if t== args.num_steps - 1:
+                            nextnonterminal = 1.0 -next_done
+                            next_return = next_value
+                        else:
+                            nextnonterminal = 1.0 - dones[t+1]
+                            next_return = returns[t+1]
+                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
+                    advantages = returns - values
+            
+            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
+            b_logprobs =logprobs.reshape(-1)
+            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
+            b_advantages =advantages.reshape(-1)
+            b_returns = returns.reshape(-1)
+            b_values = values.reshape(-1)
+
+            # Optimizaing the policy and value network
+            b_inds = np.arange(args.batch_size)
+            clipfracs = []
+            for epoch in range(args.update_epochs):
+                np.random.shuffle(b_inds)
+                for start in range(0,args.batch_size, args.minibatch_size):
+                    end = start + args.minibatch_size
+                    mb_inds = b_inds[start:end]
+
+                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
+                        b_obs[mb_inds], b_actions.long()[mb_inds]
+                    )
+                    logratio = newlogprob - b_logprobs[mb_inds]
+                    ratio = logratio.exp()
+
+                    with torch.no_grad():
+                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
+                        old_approx_kl = (-logratio).mean()
+                        approx_kl =((ratio-1) - logratio).mean()
+                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
+
+                    mb_advantages = b_advantages[mb_inds]
+                    if args.norm_adv:
+                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
+
+                    # Policy loss
+                    pg_loss1 = -mb_advantages * ratio
+                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
+                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
+
+                    # Value loss
+                    newvalue = newvalue.view(-1)
+                    if args.clip_vloss:
+                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
+                        v_clipped = b_values[mb_inds] + torch.clamp(
+                            newvalue - b_values[mb_inds],
+                            -args.clip_coef,
+                            args.clip_coef
+                        )
+                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
+                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
+                        v_loss = 0.5 * v_loss_max.mean()
+                    else:
+                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
+                    
+                    # Entropy loss
+                    entropy_loss = entropy.mean()
+                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
+
+                    optimizer.zero_grad()
+                    loss.backward()
+                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
+                    optimizer.step()
+            
+                if args.target_kl is not None:
+                    if approx_kl > args.target_kl:
+                        break
+
+        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
+        var_y = np.var(y_true)
+        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
+
+        # TRY NOT TO MODIFY: record rewards for plotting purposes
+        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
+        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
+        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
+        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
+        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
+        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
+        writer.add_scalar("losses/explained_variance",explained_var, global_step)
+        print("SPS:", int(global_step / (time.time() - start_time)))
+        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
+
+    envs.close()
+    writer.close()
\ No newline at end of file
diff --git a/wandb/run-20260421_155431-m5qntrul/files/config.yaml b/wandb/run-20260421_155431-m5qntrul/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8d8c1f67c6f1c6a0fc0527c8db78bd74d61a5907
--- /dev/null
+++ b/wandb/run-20260421_155431-m5qntrul/files/config.yaml
@@ -0,0 +1,103 @@
+_wandb:
+    value:
+        cli_version: 0.26.0
+        code_path: code/ppo.py
+        e:
+            dhgsq1l46wv1id5nwgm4sprzoqm8wbah:
+                args:
+                    - --track
+                    - --capture-video
+                codePath: ppo.py
+                codePathLocal: ppo.py
+                cpu_count: 12
+                cpu_count_logical: 14
+                disk:
+                    /:
+                        total: "499680116736"
+                        used: "141826605056"
+                email: loisfernandezcmp@gmail.com
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+                git:
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+                    remote: https://github.com/loisonchambers/Unit-8-RL-course.git
+                host: BL-LFERNANDEZ.local.eurecat.org
+                memory:
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+                program: /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py
+                python: CPython 3.9.5
+                root: /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8
+                startedAt: "2026-04-21T13:54:31.637603Z"
+                writerId: dhgsq1l46wv1id5nwgm4sprzoqm8wbah
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+learning_rate:
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+max_grad_norm:
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+minibatch_size:
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+norm_adv:
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+num_envs:
+    value: 4
+num_minibatches:
+    value: 4
+num_steps:
+    value: 128
+seed:
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+    value: null
+torch_deterministic:
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+total_timesteps:
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diff --git a/wandb/run-20260421_155431-m5qntrul/files/diff.patch b/wandb/run-20260421_155431-m5qntrul/files/diff.patch
new file mode 100644
index 0000000000000000000000000000000000000000..c257a844997a3863402696d66cfa25112c081546
--- /dev/null
+++ b/wandb/run-20260421_155431-m5qntrul/files/diff.patch
@@ -0,0 +1,1316 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..4c3cf56 100644
+--- a/poetry.lock
++++ b/poetry.lock
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++]
++
++[[package]]
++name = "click"
++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
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++    {file = "click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a"},
++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "eval_type_backport-0.3.1-py3-none-any.whl", hash = "sha256:279ab641905e9f11129f56a8a78f493518515b83402b860f6f06dd7c011fdfa8"},
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++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
++optional = false
++python-versions = ">=3.7"
++files = [
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++    {file = "gitdb-4.0.12.tar.gz", hash = "sha256:5ef71f855d191a3326fcfbc0d5da835f26b13fbcba60c32c21091c349ffdb571"},
++]
++
++[package.dependencies]
++smmap = ">=3.0.1,<6"
++
++[[package]]
++name = "gitpython"
++version = "3.1.46"
++description = "GitPython is a Python library used to interact with Git repositories"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "gitpython-3.1.46-py3-none-any.whl", hash = "sha256:79812ed143d9d25b6d176a10bb511de0f9c67b1fa641d82097b0ab90398a2058"},
++    {file = "gitpython-3.1.46.tar.gz", hash = "sha256:400124c7d0ef4ea03f7310ac2fbf7151e09ff97f2a3288d64a440c584a29c37f"},
++]
++
++[package.dependencies]
++gitdb = ">=4.0.1,<5"
++typing-extensions = {version = ">=3.10.0.2", markers = "python_version < \"3.10\""}
++
++[package.extras]
++doc = ["sphinx (>=7.1.2,<7.2)", "sphinx-autodoc-typehints", "sphinx_rtd_theme"]
++test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy (==1.18.2)", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
++
+ [[package]]
+ name = "grpcio"
+ version = "1.80.0"
+@@ -303,6 +524,45 @@ typing-extensions = ">=4.12,<5.0"
+ [package.extras]
+ protobuf = ["grpcio-tools (>=1.80.0)"]
+ 
++[[package]]
++name = "gym"
++version = "0.25.2"
++description = "Gym: A universal API for reinforcement learning environments"
++optional = false
++python-versions = ">=3.6"
++files = [
++    {file = "gym-0.25.2.tar.gz", hash = "sha256:c8323f4c6f37363710b519742a0094316deddc9d8fc9e3f6deac08d6da47150c"},
++]
++
++[package.dependencies]
++cloudpickle = ">=1.2.0"
++gym_notices = ">=0.0.4"
++importlib_metadata = {version = ">=4.8.0", markers = "python_version < \"3.10\""}
++numpy = ">=1.18.0"
++
++[package.extras]
++accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"]
++all = ["ale-py (>=0.7.5,<0.8.0)", "box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "mujoco (==2.2.0)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)", "swig (==4.*)"]
++atari = ["ale-py (>=0.7.5,<0.8.0)"]
++box2d = ["box2d-py (==2.3.5)", "pygame (==2.1.0)", "swig (==4.*)"]
++classic-control = ["pygame (==2.1.0)"]
++mujoco = ["imageio (>=2.14.1)", "mujoco (==2.2.0)"]
++mujoco-py = ["mujoco_py (>=2.1,<2.2)"]
++other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "opencv-python (>=3.0)"]
++testing = ["box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "mujoco (==2.2.0)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)", "swig (==4.*)"]
++toy-text = ["pygame (==2.1.0)"]
++
++[[package]]
++name = "gym-notices"
++version = "0.1.0"
++description = "Notices for gym"
++optional = false
++python-versions = "*"
++files = [
++    {file = "gym_notices-0.1.0-py3-none-any.whl", hash = "sha256:a943af4446cb619d04fd1e470b9272b4473e08a06d1c7cc9005755a4a0b8c905"},
++    {file = "gym_notices-0.1.0.tar.gz", hash = "sha256:9f9477ef68a8c15e42625d4fa53631237e3e6ae947f325b5c149c081499adc1b"},
++]
++
+ [[package]]
+ name = "gymnasium"
+ version = "0.29.1"
+@@ -334,6 +594,36 @@ other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-pyt
+ testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
+ toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+ 
++[[package]]
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++description = "Internationalized Domain Names in Applications (IDNA)"
++optional = false
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++
++[[package]]
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+ 
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+ 
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+@@ -919,6 +1192,22 @@ tests = ["check-manifest", "coverage (>=7.4.2)", "defusedxml", "markdown2", "ole
+ typing = ["typing-extensions"]
+ xmp = ["defusedxml"]
+ 
++[[package]]
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++
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++type = ["mypy (>=1.14.1)"]
++
+ [[package]]
+ name = "pluggy"
+ version = "1.6.0"
+@@ -953,6 +1242,228 @@ files = [
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+ 
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++
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++    {file = "sentry_sdk-2.58.0-py2.py3-none-any.whl", hash = "sha256:688d1c704ddecf382ea3326f21a67453d4caa95592d722b7c780a36a9d23109e"},
++    {file = "sentry_sdk-2.58.0.tar.gz", hash = "sha256:c1144d947352d54e5b7daa63596d9f848adf684989c06c4f5a659f0c85a18f6f"},
++]
++
++[package.dependencies]
++certifi = "*"
++urllib3 = ">=1.26.11"
++
++[package.extras]
++aiohttp = ["aiohttp (>=3.5)"]
++anthropic = ["anthropic (>=0.16)"]
++arq = ["arq (>=0.23)"]
++asyncio = ["httpcore[asyncio] (==1.*)"]
++asyncpg = ["asyncpg (>=0.23)"]
++beam = ["apache-beam (>=2.12)"]
++bottle = ["bottle (>=0.12.13)"]
++celery = ["celery (>=3)"]
++celery-redbeat = ["celery-redbeat (>=2)"]
++chalice = ["chalice (>=1.16.0)"]
++clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
++django = ["django (>=1.8)"]
++falcon = ["falcon (>=1.4)"]
++fastapi = ["fastapi (>=0.79.0)"]
++flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
++google-genai = ["google-genai (>=1.29.0)"]
++grpcio = ["grpcio (>=1.21.1)", "protobuf (>=3.8.0)"]
++http2 = ["httpcore[http2] (==1.*)"]
++httpx = ["httpx (>=0.16.0)"]
++huey = ["huey (>=2)"]
++huggingface-hub = ["huggingface_hub (>=0.22)"]
++langchain = ["langchain (>=0.0.210)"]
++langgraph = ["langgraph (>=0.6.6)"]
++launchdarkly = ["launchdarkly-server-sdk (>=9.8.0)"]
++litellm = ["litellm (>=1.77.5,!=1.82.7,!=1.82.8)"]
++litestar = ["litestar (>=2.0.0)"]
++loguru = ["loguru (>=0.5)"]
++mcp = ["mcp (>=1.15.0)"]
++openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
++openfeature = ["openfeature-sdk (>=0.7.1)"]
++opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
++opentelemetry-experimental = ["opentelemetry-distro"]
++opentelemetry-otlp = ["opentelemetry-distro[otlp] (>=0.35b0)"]
++pure-eval = ["asttokens", "executing", "pure_eval"]
++pydantic-ai = ["pydantic-ai (>=1.0.0)"]
++pymongo = ["pymongo (>=3.1)"]
++pyspark = ["pyspark (>=2.4.4)"]
++quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
++rq = ["rq (>=0.6)"]
++sanic = ["sanic (>=0.8)"]
++sqlalchemy = ["sqlalchemy (>=1.2)"]
++starlette = ["starlette (>=0.19.1)"]
++starlite = ["starlite (>=1.48)"]
++statsig = ["statsig (>=0.55.3)"]
++tornado = ["tornado (>=6)"]
++unleash = ["UnleashClient (>=6.0.1)"]
++
++[[package]]
++name = "setuptools"
++version = "65.5.0"
++description = "Easily download, build, install, upgrade, and uninstall Python packages"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "setuptools-65.5.0-py3-none-any.whl", hash = "sha256:f62ea9da9ed6289bfe868cd6845968a2c854d1427f8548d52cae02a42b4f0356"},
++    {file = "setuptools-65.5.0.tar.gz", hash = "sha256:512e5536220e38146176efb833d4a62aa726b7bbff82cfbc8ba9eaa3996e0b17"},
++]
++
++[package.extras]
++docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
++testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
++testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
++
++[[package]]
++name = "smmap"
++version = "5.0.3"
++description = "A pure Python implementation of a sliding window memory map manager"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "smmap-5.0.3-py3-none-any.whl", hash = "sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f"},
++    {file = "smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c"},
++]
+ 
+ [[package]]
+ name = "sympy"
+@@ -1197,6 +1881,81 @@ files = [
+     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+ ]
+ 
++[[package]]
++name = "typing-inspection"
++version = "0.4.2"
++description = "Runtime typing introspection tools"
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
++    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
++]
++
++[package.dependencies]
++typing-extensions = ">=4.12.0"
++
++[[package]]
++name = "urllib3"
++version = "2.6.3"
++description = "HTTP library with thread-safe connection pooling, file post, and more."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
++brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"]
++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:1ece94a2a5eda1d0e3a2d8a2fd28aa0187705d6efa5ac4c0b8680083583b7ec1"},
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:92f6f303fe2af50e3f711833a835150f9b4d8082874bfd9868cf15491ea2947e"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:a17aae051a31831388cff880251c1b5bc38fbf6a283a0ee7c543709e8e9633d1"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:28a14ffc014e523498d077dfde12839b7be586ca8c3190e72e7167c1aea6ee4c"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1995,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "89750cd6ebb50c516550a1896508559bba968e0ca72314d4dfa46ba5469fa298"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..29673f1 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -2,8 +2,62 @@ import argparse
+ import os
+ from distutils.util import strtobool
+ import time
++import random
++import numpy as np
++import gym
++import torch
++import torch.nn as nn
++import torch.optim as optim
++from torch.distributions.categorical import Categorical
+ from torch.utils.tensorboard import SummaryWriter
+ 
++def make_env(gym_id, seed, idx, capture_video, run_name):
++    def thunk():
++        env = gym.make(gym_id)
++        env = gym.wrappers.RecordEpisodeStatistics(env)
++        if capture_video:
++            if idx == 0:
++                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", record_video_trigger=lambda t: t % 1000 == 0)
++        env.seed(seed)
++        env.action_space.seed(seed)
++        env.observation_space.seed(seed)
++        return env
++    return thunk
++
++def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
++    torch.nn.init.orthogonal_(layer.weight, std)
++    torch.nn.init.constant_(layer.bias, bias_const)
++    return layer
++
++class Agent(nn.Module):
++    def __init__(self, envs):
++        super(Agent, self).__init__()
++        self.critic = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,1), std=1.),
++        )
++
++        self.actor = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
++        )
++
++    def get_value(self, x):
++        return self.critic(x)
++    
++    def get_action_and_value(self, x, action=None):
++        logits = self.actor(x)
++        probs = Categorical(logits=logits)
++        if action is None:
++            action = probs.sample()
++        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
++
+ def parse_args():
+     parser = argparse.ArgumentParser()
+     parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+@@ -16,21 +70,244 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
++    parser.add_argument("--num-envs", type=int, default=4,
++    help="the number of parallel game environments")
++    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
++        help="whether to capture videos of the agent performances (check out `videos` folder)")
++    parser.add_argument("--num-steps", type=int, default=128,
++        help="the number of steps to run in each environment per policy rollout")
++    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggle learning rate annealing for policy and value networks")  
++    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Use GAE for advantage computation")  
++    parser.add_argument('--gamma', type=float, default=0.99,
++        help='the discount factor gamma')
++    parser.add_argument('--gae-lambda', type=float, default=0.95,
++        help='the lambda for the general advantage estimation')
++    parser.add_argument("--num-minibatches", type=int, default=4,
++        help="the number of mini-batches")
++    parser.add_argument("--update-epochs", type=int, default=4,
++        help="the K epochs to update the policy")
++    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles advantages normalization")
++    parser.add_argument('--clip-coef', type=float, default=0.2,
++        help='the surrogate clipping coefficient')     
++    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
++    parser.add_argument('--ent-coef', type=float, default=0.01,
++        help='coefficient of the entropy')
++    parser.add_argument('--vf-coef', type=float, default=0.5,
++        help='coefficient of the value function')
++    parser.add_argument('--max-grad-norm', type=float, default=0.5,
++        help='the maximum norm for the gradient clipping')
++    parser.add_argument('--target-kl', type=float, default=None,
++        help='the target KL divergence threshold')
+     args = parser.parse_args()
++    args.batch_size = int(args.num_envs * args.num_steps)
++    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    envs = gym.vector.SyncVectorEnv(
++        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
++    for i in range(args.num_envs)])
++    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
++
++    agent = Agent(envs).to(device)
++    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
++    
++    
++    # ALGO Logic: Storage setup
++    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
++    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
++    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
++
++    #TRY NOT TO MODIFY: start the game
++    global_step = 0
++    start_time =time.time()
++    next_obs = torch.Tensor(envs.reset()).to(device)
++    next_done = torch.zeros(args.num_envs).to(device)
++    num_updates = args.total_timesteps // args.batch_size
++
++    for update in range(1, num_updates+1):
++        # Annealing the rate if instructed to do so.abs
++        if args.anneal_lr:
++            frac = 1.0 -(update -1.0) / num_updates
++            lrnow = frac * args.learning_rate
++            optimizer.param_groups[0]["lr"]=lrnow
++        
++        for step in range(0, args.num_steps):
++            global_step += 1 * args.num_envs
++            obs[step] = next_obs
++            dones[step] = next_done
++
++            # ALGO LOGIC : action logic
++            with torch.no_grad():
++                action, logprob, _, value = agent.get_action_and_value(next_obs)
++                values[step] = value.flatten()
++            actions[step] = action
++            logprobs[step] = logprob
++
++            # TRY NOT TO MODIFY: execute the game and log data.abs
++            next_obs, reward, done, info = envs.step(action.cpu().numpy())
++            rewards[step] = torch.tensor(reward).to(device).view(-1)
++            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
++        
++            if isinstance(info, dict) and "episode" in info:
++                for item in info["episode"]:
++                    if item is not None:
++                        print(f"global_step={global_step}, episodic_return={item['r']}")
++                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
++                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
++                        break
++            
++            #bootstrap reward if not done
++            with torch.no_grad():
++                next_value = agent.get_value(next_obs).reshape(1,-1)
++                if args.gae:
++                    advantages = torch.zeros_like(rewards).to(device)
++                    lastgaelam = 0
++                    for t in reversed(range(args.num_steps)):
++                        if t == args.num_steps - 1:
++                            nextnonterminal = 1.0 - next_done
++                            nextvalues = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            nextvalues = values[t+1]
++                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
++                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
++                    returns =advantages + values
++                else:
++                    returns =torch.zeros_like(rewards).to(device)
++                    for t in reversed(range(args.num_steps)):
++                        if t== args.num_steps - 1:
++                            nextnonterminal = 1.0 -next_done
++                            next_return = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            next_return = returns[t+1]
++                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
++                    advantages = returns - values
++            
++            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
++            b_logprobs =logprobs.reshape(-1)
++            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
++            b_advantages =advantages.reshape(-1)
++            b_returns = returns.reshape(-1)
++            b_values = values.reshape(-1)
++
++            # Optimizaing the policy and value network
++            b_inds = np.arange(args.batch_size)
++            clipfracs = []
++            for epoch in range(args.update_epochs):
++                np.random.shuffle(b_inds)
++                for start in range(0,args.batch_size, args.minibatch_size):
++                    end = start + args.minibatch_size
++                    mb_inds = b_inds[start:end]
++
++                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
++                        b_obs[mb_inds], b_actions.long()[mb_inds]
++                    )
++                    logratio = newlogprob - b_logprobs[mb_inds]
++                    ratio = logratio.exp()
++
++                    with torch.no_grad():
++                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
++                        old_approx_kl = (-logratio).mean()
++                        approx_kl =((ratio-1) - logratio).mean()
++                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
++
++                    mb_advantages = b_advantages[mb_inds]
++                    if args.norm_adv:
++                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
++
++                    # Policy loss
++                    pg_loss1 = -mb_advantages * ratio
++                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
++                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
++
++                    # Value loss
++                    newvalue = newvalue.view(-1)
++                    if args.clip_vloss:
++                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
++                        v_clipped = b_values[mb_inds] + torch.clamp(
++                            newvalue - b_values[mb_inds],
++                            -args.clip_coef,
++                            args.clip_coef
++                        )
++                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
++                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
++                        v_loss = 0.5 * v_loss_max.mean()
++                    else:
++                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
++                    
++                    # Entropy loss
++                    entropy_loss = entropy.mean()
++                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
++
++                    optimizer.zero_grad()
++                    loss.backward()
++                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
++                    optimizer.step()
++            
++                if args.target_kl is not None:
++                    if approx_kl > args.target_kl:
++                        break
++
++        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
++        var_y = np.var(y_true)
++        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
++
++        # TRY NOT TO MODIFY: record rewards for plotting purposes
++        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
++        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
++        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
++        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
++        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
++        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
++        writer.add_scalar("losses/explained_variance",explained_var, global_step)
++        print("SPS:", int(global_step / (time.time() - start_time)))
++        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
++
++    envs.close()
++    writer.close()
+\ No newline at end of file
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..eb4a63c 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.25.2"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_155431-m5qntrul/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch b/wandb/run-20260421_155431-m5qntrul/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
new file mode 100644
index 0000000000000000000000000000000000000000..c257a844997a3863402696d66cfa25112c081546
--- /dev/null
+++ b/wandb/run-20260421_155431-m5qntrul/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
@@ -0,0 +1,1316 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..4c3cf56 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
++    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"},
++    {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"},
++]
++
++[[package]]
++name = "charset-normalizer"
++version = "3.4.7"
++description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "charset_normalizer-3.4.7-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:cdd68a1fb318e290a2077696b7eb7a21a49163c455979c639bf5a5dcdc46617d"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e17b8d5d6a8c47c85e68ca8379def1303fd360c3e22093a807cd34a71cd082b8"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:511ef87c8aec0783e08ac18565a16d435372bc1ac25a91e6ac7f5ef2b0bff790"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:007d05ec7321d12a40227aae9e2bc6dca73f3cb21058999a1df9e193555a9dcc"},
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++
++[[package]]
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++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++smmap = ">=3.0.1,<6"
++
++[[package]]
++name = "gitpython"
++version = "3.1.46"
++description = "GitPython is a Python library used to interact with Git repositories"
++optional = false
++python-versions = ">=3.7"
++files = [
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++    {file = "gitpython-3.1.46.tar.gz", hash = "sha256:400124c7d0ef4ea03f7310ac2fbf7151e09ff97f2a3288d64a440c584a29c37f"},
++]
++
++[package.dependencies]
++gitdb = ">=4.0.1,<5"
++typing-extensions = {version = ">=3.10.0.2", markers = "python_version < \"3.10\""}
++
++[package.extras]
++doc = ["sphinx (>=7.1.2,<7.2)", "sphinx-autodoc-typehints", "sphinx_rtd_theme"]
++test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy (==1.18.2)", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
++
+ [[package]]
+ name = "grpcio"
+ version = "1.80.0"
+@@ -303,6 +524,45 @@ typing-extensions = ">=4.12,<5.0"
+ [package.extras]
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+ 
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++
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++
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+ 
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+ typing = ["typing-extensions"]
+ xmp = ["defusedxml"]
+ 
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++
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++]
++
++[[package]]
++name = "requests"
++version = "2.32.5"
++description = "Python HTTP for Humans."
+ optional = false
+ python-versions = ">=3.9"
+ files = [
+-    {file = "setuptools-82.0.1-py3-none-any.whl", hash = "sha256:a59e362652f08dcd477c78bb6e7bd9d80a7995bc73ce773050228a348ce2e5bb"},
+-    {file = "setuptools-82.0.1.tar.gz", hash = "sha256:7d872682c5d01cfde07da7bccc7b65469d3dca203318515ada1de5eda35efbf9"},
++    {file = "requests-2.32.5-py3-none-any.whl", hash = "sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6"},
++    {file = "requests-2.32.5.tar.gz", hash = "sha256:dbba0bac56e100853db0ea71b82b4dfd5fe2bf6d3754a8893c3af500cec7d7cf"},
+ ]
+ 
++[package.dependencies]
++certifi = ">=2017.4.17"
++charset_normalizer = ">=2,<4"
++idna = ">=2.5,<4"
++urllib3 = ">=1.21.1,<3"
++
+ [package.extras]
+-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.13.0)"]
+-core = ["importlib_metadata (>=6)", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more_itertools", "more_itertools (>=8.8)", "packaging (>=24.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
+-cover = ["pytest-cov"]
+-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+-enabler = ["pytest-enabler (>=2.2)"]
+-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+-type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.18.*)", "pytest-mypy"]
++socks = ["PySocks (>=1.5.6,!=1.5.7)"]
++use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
++
++[[package]]
++name = "sentry-sdk"
++version = "2.58.0"
++description = "Python client for Sentry (https://sentry.io)"
++optional = false
++python-versions = ">=3.6"
++files = [
++    {file = "sentry_sdk-2.58.0-py2.py3-none-any.whl", hash = "sha256:688d1c704ddecf382ea3326f21a67453d4caa95592d722b7c780a36a9d23109e"},
++    {file = "sentry_sdk-2.58.0.tar.gz", hash = "sha256:c1144d947352d54e5b7daa63596d9f848adf684989c06c4f5a659f0c85a18f6f"},
++]
++
++[package.dependencies]
++certifi = "*"
++urllib3 = ">=1.26.11"
++
++[package.extras]
++aiohttp = ["aiohttp (>=3.5)"]
++anthropic = ["anthropic (>=0.16)"]
++arq = ["arq (>=0.23)"]
++asyncio = ["httpcore[asyncio] (==1.*)"]
++asyncpg = ["asyncpg (>=0.23)"]
++beam = ["apache-beam (>=2.12)"]
++bottle = ["bottle (>=0.12.13)"]
++celery = ["celery (>=3)"]
++celery-redbeat = ["celery-redbeat (>=2)"]
++chalice = ["chalice (>=1.16.0)"]
++clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
++django = ["django (>=1.8)"]
++falcon = ["falcon (>=1.4)"]
++fastapi = ["fastapi (>=0.79.0)"]
++flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
++google-genai = ["google-genai (>=1.29.0)"]
++grpcio = ["grpcio (>=1.21.1)", "protobuf (>=3.8.0)"]
++http2 = ["httpcore[http2] (==1.*)"]
++httpx = ["httpx (>=0.16.0)"]
++huey = ["huey (>=2)"]
++huggingface-hub = ["huggingface_hub (>=0.22)"]
++langchain = ["langchain (>=0.0.210)"]
++langgraph = ["langgraph (>=0.6.6)"]
++launchdarkly = ["launchdarkly-server-sdk (>=9.8.0)"]
++litellm = ["litellm (>=1.77.5,!=1.82.7,!=1.82.8)"]
++litestar = ["litestar (>=2.0.0)"]
++loguru = ["loguru (>=0.5)"]
++mcp = ["mcp (>=1.15.0)"]
++openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
++openfeature = ["openfeature-sdk (>=0.7.1)"]
++opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
++opentelemetry-experimental = ["opentelemetry-distro"]
++opentelemetry-otlp = ["opentelemetry-distro[otlp] (>=0.35b0)"]
++pure-eval = ["asttokens", "executing", "pure_eval"]
++pydantic-ai = ["pydantic-ai (>=1.0.0)"]
++pymongo = ["pymongo (>=3.1)"]
++pyspark = ["pyspark (>=2.4.4)"]
++quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
++rq = ["rq (>=0.6)"]
++sanic = ["sanic (>=0.8)"]
++sqlalchemy = ["sqlalchemy (>=1.2)"]
++starlette = ["starlette (>=0.19.1)"]
++starlite = ["starlite (>=1.48)"]
++statsig = ["statsig (>=0.55.3)"]
++tornado = ["tornado (>=6)"]
++unleash = ["UnleashClient (>=6.0.1)"]
++
++[[package]]
++name = "setuptools"
++version = "65.5.0"
++description = "Easily download, build, install, upgrade, and uninstall Python packages"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "setuptools-65.5.0-py3-none-any.whl", hash = "sha256:f62ea9da9ed6289bfe868cd6845968a2c854d1427f8548d52cae02a42b4f0356"},
++    {file = "setuptools-65.5.0.tar.gz", hash = "sha256:512e5536220e38146176efb833d4a62aa726b7bbff82cfbc8ba9eaa3996e0b17"},
++]
++
++[package.extras]
++docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
++testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
++testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
++
++[[package]]
++name = "smmap"
++version = "5.0.3"
++description = "A pure Python implementation of a sliding window memory map manager"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "smmap-5.0.3-py3-none-any.whl", hash = "sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f"},
++    {file = "smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c"},
++]
+ 
+ [[package]]
+ name = "sympy"
+@@ -1197,6 +1881,81 @@ files = [
+     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+ ]
+ 
++[[package]]
++name = "typing-inspection"
++version = "0.4.2"
++description = "Runtime typing introspection tools"
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
++    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
++]
++
++[package.dependencies]
++typing-extensions = ">=4.12.0"
++
++[[package]]
++name = "urllib3"
++version = "2.6.3"
++description = "HTTP library with thread-safe connection pooling, file post, and more."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
++brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"]
++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:1ece94a2a5eda1d0e3a2d8a2fd28aa0187705d6efa5ac4c0b8680083583b7ec1"},
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:92f6f303fe2af50e3f711833a835150f9b4d8082874bfd9868cf15491ea2947e"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:a17aae051a31831388cff880251c1b5bc38fbf6a283a0ee7c543709e8e9633d1"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:28a14ffc014e523498d077dfde12839b7be586ca8c3190e72e7167c1aea6ee4c"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1995,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "89750cd6ebb50c516550a1896508559bba968e0ca72314d4dfa46ba5469fa298"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..29673f1 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -2,8 +2,62 @@ import argparse
+ import os
+ from distutils.util import strtobool
+ import time
++import random
++import numpy as np
++import gym
++import torch
++import torch.nn as nn
++import torch.optim as optim
++from torch.distributions.categorical import Categorical
+ from torch.utils.tensorboard import SummaryWriter
+ 
++def make_env(gym_id, seed, idx, capture_video, run_name):
++    def thunk():
++        env = gym.make(gym_id)
++        env = gym.wrappers.RecordEpisodeStatistics(env)
++        if capture_video:
++            if idx == 0:
++                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", record_video_trigger=lambda t: t % 1000 == 0)
++        env.seed(seed)
++        env.action_space.seed(seed)
++        env.observation_space.seed(seed)
++        return env
++    return thunk
++
++def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
++    torch.nn.init.orthogonal_(layer.weight, std)
++    torch.nn.init.constant_(layer.bias, bias_const)
++    return layer
++
++class Agent(nn.Module):
++    def __init__(self, envs):
++        super(Agent, self).__init__()
++        self.critic = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,1), std=1.),
++        )
++
++        self.actor = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
++        )
++
++    def get_value(self, x):
++        return self.critic(x)
++    
++    def get_action_and_value(self, x, action=None):
++        logits = self.actor(x)
++        probs = Categorical(logits=logits)
++        if action is None:
++            action = probs.sample()
++        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
++
+ def parse_args():
+     parser = argparse.ArgumentParser()
+     parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+@@ -16,21 +70,244 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
++    parser.add_argument("--num-envs", type=int, default=4,
++    help="the number of parallel game environments")
++    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
++        help="whether to capture videos of the agent performances (check out `videos` folder)")
++    parser.add_argument("--num-steps", type=int, default=128,
++        help="the number of steps to run in each environment per policy rollout")
++    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggle learning rate annealing for policy and value networks")  
++    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Use GAE for advantage computation")  
++    parser.add_argument('--gamma', type=float, default=0.99,
++        help='the discount factor gamma')
++    parser.add_argument('--gae-lambda', type=float, default=0.95,
++        help='the lambda for the general advantage estimation')
++    parser.add_argument("--num-minibatches", type=int, default=4,
++        help="the number of mini-batches")
++    parser.add_argument("--update-epochs", type=int, default=4,
++        help="the K epochs to update the policy")
++    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles advantages normalization")
++    parser.add_argument('--clip-coef', type=float, default=0.2,
++        help='the surrogate clipping coefficient')     
++    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
++    parser.add_argument('--ent-coef', type=float, default=0.01,
++        help='coefficient of the entropy')
++    parser.add_argument('--vf-coef', type=float, default=0.5,
++        help='coefficient of the value function')
++    parser.add_argument('--max-grad-norm', type=float, default=0.5,
++        help='the maximum norm for the gradient clipping')
++    parser.add_argument('--target-kl', type=float, default=None,
++        help='the target KL divergence threshold')
+     args = parser.parse_args()
++    args.batch_size = int(args.num_envs * args.num_steps)
++    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    envs = gym.vector.SyncVectorEnv(
++        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
++    for i in range(args.num_envs)])
++    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
++
++    agent = Agent(envs).to(device)
++    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
++    
++    
++    # ALGO Logic: Storage setup
++    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
++    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
++    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
++
++    #TRY NOT TO MODIFY: start the game
++    global_step = 0
++    start_time =time.time()
++    next_obs = torch.Tensor(envs.reset()).to(device)
++    next_done = torch.zeros(args.num_envs).to(device)
++    num_updates = args.total_timesteps // args.batch_size
++
++    for update in range(1, num_updates+1):
++        # Annealing the rate if instructed to do so.abs
++        if args.anneal_lr:
++            frac = 1.0 -(update -1.0) / num_updates
++            lrnow = frac * args.learning_rate
++            optimizer.param_groups[0]["lr"]=lrnow
++        
++        for step in range(0, args.num_steps):
++            global_step += 1 * args.num_envs
++            obs[step] = next_obs
++            dones[step] = next_done
++
++            # ALGO LOGIC : action logic
++            with torch.no_grad():
++                action, logprob, _, value = agent.get_action_and_value(next_obs)
++                values[step] = value.flatten()
++            actions[step] = action
++            logprobs[step] = logprob
++
++            # TRY NOT TO MODIFY: execute the game and log data.abs
++            next_obs, reward, done, info = envs.step(action.cpu().numpy())
++            rewards[step] = torch.tensor(reward).to(device).view(-1)
++            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
++        
++            if isinstance(info, dict) and "episode" in info:
++                for item in info["episode"]:
++                    if item is not None:
++                        print(f"global_step={global_step}, episodic_return={item['r']}")
++                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
++                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
++                        break
++            
++            #bootstrap reward if not done
++            with torch.no_grad():
++                next_value = agent.get_value(next_obs).reshape(1,-1)
++                if args.gae:
++                    advantages = torch.zeros_like(rewards).to(device)
++                    lastgaelam = 0
++                    for t in reversed(range(args.num_steps)):
++                        if t == args.num_steps - 1:
++                            nextnonterminal = 1.0 - next_done
++                            nextvalues = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            nextvalues = values[t+1]
++                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
++                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
++                    returns =advantages + values
++                else:
++                    returns =torch.zeros_like(rewards).to(device)
++                    for t in reversed(range(args.num_steps)):
++                        if t== args.num_steps - 1:
++                            nextnonterminal = 1.0 -next_done
++                            next_return = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            next_return = returns[t+1]
++                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
++                    advantages = returns - values
++            
++            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
++            b_logprobs =logprobs.reshape(-1)
++            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
++            b_advantages =advantages.reshape(-1)
++            b_returns = returns.reshape(-1)
++            b_values = values.reshape(-1)
++
++            # Optimizaing the policy and value network
++            b_inds = np.arange(args.batch_size)
++            clipfracs = []
++            for epoch in range(args.update_epochs):
++                np.random.shuffle(b_inds)
++                for start in range(0,args.batch_size, args.minibatch_size):
++                    end = start + args.minibatch_size
++                    mb_inds = b_inds[start:end]
++
++                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
++                        b_obs[mb_inds], b_actions.long()[mb_inds]
++                    )
++                    logratio = newlogprob - b_logprobs[mb_inds]
++                    ratio = logratio.exp()
++
++                    with torch.no_grad():
++                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
++                        old_approx_kl = (-logratio).mean()
++                        approx_kl =((ratio-1) - logratio).mean()
++                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
++
++                    mb_advantages = b_advantages[mb_inds]
++                    if args.norm_adv:
++                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
++
++                    # Policy loss
++                    pg_loss1 = -mb_advantages * ratio
++                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
++                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
++
++                    # Value loss
++                    newvalue = newvalue.view(-1)
++                    if args.clip_vloss:
++                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
++                        v_clipped = b_values[mb_inds] + torch.clamp(
++                            newvalue - b_values[mb_inds],
++                            -args.clip_coef,
++                            args.clip_coef
++                        )
++                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
++                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
++                        v_loss = 0.5 * v_loss_max.mean()
++                    else:
++                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
++                    
++                    # Entropy loss
++                    entropy_loss = entropy.mean()
++                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
++
++                    optimizer.zero_grad()
++                    loss.backward()
++                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
++                    optimizer.step()
++            
++                if args.target_kl is not None:
++                    if approx_kl > args.target_kl:
++                        break
++
++        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
++        var_y = np.var(y_true)
++        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
++
++        # TRY NOT TO MODIFY: record rewards for plotting purposes
++        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
++        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
++        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
++        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
++        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
++        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
++        writer.add_scalar("losses/explained_variance",explained_var, global_step)
++        print("SPS:", int(global_step / (time.time() - start_time)))
++        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
++
++    envs.close()
++    writer.close()
+\ No newline at end of file
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..eb4a63c 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.25.2"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_155431-m5qntrul/files/output.log b/wandb/run-20260421_155431-m5qntrul/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..32c0302182023fcefffe6da65470a58a8c97acd0
--- /dev/null
+++ b/wandb/run-20260421_155431-m5qntrul/files/output.log
@@ -0,0 +1,30 @@
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py:317: DeprecationWarning: WARN: Initializing wrapper in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.
+  deprecation(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/step_api_compatibility.py:39: DeprecationWarning: WARN: Initializing environment in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.
+  deprecation(
+Exception ignored in: <function RecordVideo.__del__ at 0x7833ffb26d30>
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/record_video.py", line 219, in __del__
+    self.close_video_recorder()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/record_video.py", line 186, in close_video_recorder
+    if self.recording:
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py", line 325, in __getattr__
+    return getattr(self.env, name)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py", line 325, in __getattr__
+    return getattr(self.env, name)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py", line 325, in __getattr__
+    return getattr(self.env, name)
+  [Previous line repeated 988 more times]
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py", line 323, in __getattr__
+    if name.startswith("_"):
+RecursionError: maximum recursion depth exceeded while calling a Python object
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py", line 148, in <module>
+    envs = gym.vector.SyncVectorEnv(
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/vector/sync_vector_env.py", line 54, in __init__
+    self.envs = [env_fn() for env_fn in env_fns]
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/vector/sync_vector_env.py", line 54, in <listcomp>
+    self.envs = [env_fn() for env_fn in env_fns]
+  File "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py", line 20, in thunk
+    env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", record_video_trigger=lambda t: t % 1000 == 0)
+TypeError: __init__() got an unexpected keyword argument 'record_video_trigger'
diff --git a/wandb/run-20260421_155431-m5qntrul/files/requirements.txt b/wandb/run-20260421_155431-m5qntrul/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3f020f4ecd61d8f6a97ef8435a2e387b50f5a3c3
--- /dev/null
+++ b/wandb/run-20260421_155431-m5qntrul/files/requirements.txt
@@ -0,0 +1,70 @@
+mpmath==1.3.0
+nvidia-nvtx-cu12==12.6.77
+pluggy==1.6.0
+charset-normalizer==3.4.7
+Markdown==3.9
+protobuf==6.33.6
+Werkzeug==3.1.8
+gym-notices==0.1.0
+tomli==2.4.1
+GitPython==3.1.46
+nvidia-cuda-cupti-cu12==12.6.80
+MarkupSafe==3.0.3
+certifi==2026.2.25
+nvidia-cublas-cu12==12.6.4.1
+nvidia-cusparselt-cu12==0.6.3
+networkx==3.2.1
+wandb==0.26.0
+tensorboard==2.20.0
+setuptools==65.5.0
+nvidia-cusparse-cu12==12.5.4.2
+gitdb==4.0.12
+pip==24.0
+Jinja2==3.1.6
+pydantic==2.13.3
+coverage==7.10.7
+sentry-sdk==2.58.0
+exceptiongroup==1.3.1
+sympy==1.14.0
+annotated-types==0.7.0
+absl-py==2.3.1
+smmap==5.0.3
+importlib_metadata==8.7.1
+idna==3.11
+pillow==11.3.0
+tensorboard-data-server==0.7.2
+cloudpickle==3.1.2
+platformdirs==4.4.0
+gym==0.25.2
+pytest==7.4.4
+nvidia-curand-cu12==10.3.7.77
+fsspec==2025.10.0
+click==8.1.8
+nvidia-cuda-nvrtc-cu12==12.6.77
+gymnasium==0.29.1
+numpy==1.23.5
+imageio-ffmpeg==0.6.0
+nvidia-nccl-cu12==2.26.2
+urllib3==2.6.3
+packaging==26.0
+zipp==3.23.1
+nvidia-cufile-cu12==1.11.1.6
+nvidia-cudnn-cu12==9.5.1.17
+triton==3.3.1
+filelock==3.19.1
+Farama-Notifications==0.0.4
+grpcio==1.80.0
+iniconfig==2.1.0
+typing_extensions==4.15.0
+PyYAML==6.0.3
+nvidia-cusolver-cu12==11.7.1.2
+nvidia-cuda-runtime-cu12==12.6.77
+pygame==2.6.1
+requests==2.32.5
+pydantic_core==2.46.3
+torch==2.7.1
+nvidia-cufft-cu12==11.3.0.4
+eval_type_backport==0.3.1
+wheel==0.38.4
+typing-inspection==0.4.2
+nvidia-nvjitlink-cu12==12.6.85
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new file mode 100644
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+++ b/wandb/run-20260421_155431-m5qntrul/files/wandb-metadata.json
@@ -0,0 +1,32 @@
+{
+  "os": "Linux-6.17.0-20-generic-x86_64-with-glibc2.39",
+  "python": "CPython 3.9.5",
+  "startedAt": "2026-04-21T13:54:31.637603Z",
+  "args": [
+    "--track",
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+  ],
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+  "codePath": "ppo.py",
+  "codePathLocal": "ppo.py",
+  "git": {
+    "remote": "https://github.com/loisonchambers/Unit-8-RL-course.git",
+    "commit": "e1dc52c8934ce180fef18fbdf53a72281f134a52"
+  },
+  "email": "loisfernandezcmp@gmail.com",
+  "root": "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8",
+  "host": "BL-LFERNANDEZ.local.eurecat.org",
+  "executable": "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/bin/python",
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diff --git a/wandb/run-20260421_155431-m5qntrul/files/wandb-summary.json b/wandb/run-20260421_155431-m5qntrul/files/wandb-summary.json
new file mode 100644
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diff --git a/wandb/run-20260421_155431-m5qntrul/logs/debug-core.log b/wandb/run-20260421_155431-m5qntrul/logs/debug-core.log
new file mode 100644
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diff --git a/wandb/run-20260421_155431-m5qntrul/logs/debug-internal.log b/wandb/run-20260421_155431-m5qntrul/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..bc1c274d654f3013e743b71dad8a65b8225cba77
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diff --git a/wandb/run-20260421_155431-m5qntrul/logs/debug.log b/wandb/run-20260421_155431-m5qntrul/logs/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..533e8cbc13b66912c911d6be41fbbbb61c375f41
--- /dev/null
+++ b/wandb/run-20260421_155431-m5qntrul/logs/debug.log
@@ -0,0 +1,22 @@
+2026-04-21 15:54:31,640 INFO    MainThread:209000 [wandb_setup.py:_flush():81] Current SDK version is 0.26.0
+2026-04-21 15:54:31,640 INFO    MainThread:209000 [wandb_setup.py:_flush():81] Configure stats pid to 209000
+2026-04-21 15:54:31,640 INFO    MainThread:209000 [wandb_setup.py:_flush():81] Loading settings from environment variables
+2026-04-21 15:54:31,640 INFO    MainThread:209000 [wandb_init.py:setup_run_log_directory():721] Logging user logs to /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/wandb/run-20260421_155431-m5qntrul/logs/debug.log
+2026-04-21 15:54:31,640 INFO    MainThread:209000 [wandb_init.py:setup_run_log_directory():722] Logging internal logs to /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/wandb/run-20260421_155431-m5qntrul/logs/debug-internal.log
+2026-04-21 15:54:31,658 INFO    MainThread:209000 [wandb_init.py:init():848] calling init triggers
+2026-04-21 15:54:31,659 INFO    MainThread:209000 [wandb_init.py:init():853] wandb.init called with sweep_config: {}
+config: {'exp_name': 'ppo', 'gym_id': 'CartPole-v1', 'learning_rate': 0.00025, 'seed': 1, 'total_timesteps': 25000, 'torch_deterministic': True, 'cuda': True, 'track': True, 'wandb_project_name': 'cleanRL', 'wandb_entity': None, 'num_envs': 4, 'capture_video': True, 'num_steps': 128, 'anneal_lr': True, 'gae': True, 'gamma': 0.99, 'gae_lambda': 0.95, 'num_minibatches': 4, 'update_epochs': 4, 'norm_adv': True, 'clip_coef': 0.2, 'clip_vloss': True, 'ent_coef': 0.01, 'vf_coef': 0.5, 'max_grad_norm': 0.5, 'target_kl': None, 'batch_size': 512, 'minibatch_size': 128, '_wandb': {'code_path': 'code/ppo.py'}}
+2026-04-21 15:54:31,659 INFO    MainThread:209000 [wandb_init.py:init():896] starting backend
+2026-04-21 15:54:31,900 INFO    MainThread:209000 [wandb_init.py:init():911] sending inform_init request
+2026-04-21 15:54:31,921 INFO    MainThread:209000 [wandb_init.py:init():919] backend started and connected
+2026-04-21 15:54:31,934 INFO    MainThread:209000 [wandb_init.py:init():989] updated telemetry
+2026-04-21 15:54:31,956 INFO    MainThread:209000 [wandb_init.py:init():1013] communicating run to backend with 90.0 second timeout
+2026-04-21 15:54:32,873 INFO    MainThread:209000 [wandb_init.py:init():1058] starting run threads in backend
+2026-04-21 15:54:32,929 INFO    MainThread:209000 [wandb_run.py:_console_start():2542] atexit reg
+2026-04-21 15:54:32,929 INFO    MainThread:209000 [wandb_run.py:_redirect():2391] redirect: wrap_raw
+2026-04-21 15:54:32,929 INFO    MainThread:209000 [wandb_run.py:_redirect():2460] Wrapping output streams.
+2026-04-21 15:54:32,929 INFO    MainThread:209000 [wandb_run.py:_redirect():2483] Redirects installed.
+2026-04-21 15:54:32,932 INFO    MainThread:209000 [wandb_init.py:init():1098] run started, returning control to user process
+2026-04-21 15:54:32,932 INFO    MainThread:209000 [wandb_run.py:_tensorboard_callback():1632] tensorboard callback: runs/CartPole-v1_ppo_1_1776779670, True
+2026-04-21 15:54:32,942 INFO    wandb-AsyncioManager-main:209000 [service_client.py:_forward_responses():134] Reached EOF.
+2026-04-21 15:54:32,943 INFO    wandb-AsyncioManager-main:209000 [mailbox.py:close():155] Closing mailbox, abandoning 1 handles.
diff --git a/wandb/run-20260421_155431-m5qntrul/run-m5qntrul.wandb b/wandb/run-20260421_155431-m5qntrul/run-m5qntrul.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..ea65d88a6dc232162a9440b14562faf33cf7f8a9
Binary files /dev/null and b/wandb/run-20260421_155431-m5qntrul/run-m5qntrul.wandb differ
diff --git a/wandb/run-20260421_155543-quaracjv/files/code/ppo.py b/wandb/run-20260421_155543-quaracjv/files/code/ppo.py
new file mode 100644
index 0000000000000000000000000000000000000000..29673f194c1e81ddfa6bf33c96f6cfd822f8473a
--- /dev/null
+++ b/wandb/run-20260421_155543-quaracjv/files/code/ppo.py
@@ -0,0 +1,313 @@
+import argparse
+import os
+from distutils.util import strtobool
+import time
+import random
+import numpy as np
+import gym
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.distributions.categorical import Categorical
+from torch.utils.tensorboard import SummaryWriter
+
+def make_env(gym_id, seed, idx, capture_video, run_name):
+    def thunk():
+        env = gym.make(gym_id)
+        env = gym.wrappers.RecordEpisodeStatistics(env)
+        if capture_video:
+            if idx == 0:
+                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", record_video_trigger=lambda t: t % 1000 == 0)
+        env.seed(seed)
+        env.action_space.seed(seed)
+        env.observation_space.seed(seed)
+        return env
+    return thunk
+
+def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
+    torch.nn.init.orthogonal_(layer.weight, std)
+    torch.nn.init.constant_(layer.bias, bias_const)
+    return layer
+
+class Agent(nn.Module):
+    def __init__(self, envs):
+        super(Agent, self).__init__()
+        self.critic = nn.Sequential(
+            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,1), std=1.),
+        )
+
+        self.actor = nn.Sequential(
+            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
+        )
+
+    def get_value(self, x):
+        return self.critic(x)
+    
+    def get_action_and_value(self, x, action=None):
+        logits = self.actor(x)
+        probs = Categorical(logits=logits)
+        if action is None:
+            action = probs.sample()
+        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+        help='the name of this experiment')
+    parser.add_argument('--gym-id', type=str, default="CartPole-v1",
+        help='the id of the gym environment')
+    parser.add_argument('--learning-rate', type=float, default=2.5e-4,
+        help='the learning rate of the optimizer')
+    parser.add_argument('--seed', type=int, default=1,
+        help='seed of the experiment')
+    parser.add_argument('--total-timesteps', type=int, default=25000,
+        help='total timesteps of the experiments')
+    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+    parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, cuda will not be enabled by default')
+    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
+        help='if toggled, this experiment will be tracked with Weights and Biases')
+    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
+        help="the wandb's project name")
+    parser.add_argument('--wandb-entity', type=str, default=None,
+        help="the entity (team) of wandb's project")
+    parser.add_argument("--num-envs", type=int, default=4,
+    help="the number of parallel game environments")
+    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="whether to capture videos of the agent performances (check out `videos` folder)")
+    parser.add_argument("--num-steps", type=int, default=128,
+        help="the number of steps to run in each environment per policy rollout")
+    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggle learning rate annealing for policy and value networks")  
+    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Use GAE for advantage computation")  
+    parser.add_argument('--gamma', type=float, default=0.99,
+        help='the discount factor gamma')
+    parser.add_argument('--gae-lambda', type=float, default=0.95,
+        help='the lambda for the general advantage estimation')
+    parser.add_argument("--num-minibatches", type=int, default=4,
+        help="the number of mini-batches")
+    parser.add_argument("--update-epochs", type=int, default=4,
+        help="the K epochs to update the policy")
+    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggles advantages normalization")
+    parser.add_argument('--clip-coef', type=float, default=0.2,
+        help='the surrogate clipping coefficient')     
+    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
+    parser.add_argument('--ent-coef', type=float, default=0.01,
+        help='coefficient of the entropy')
+    parser.add_argument('--vf-coef', type=float, default=0.5,
+        help='coefficient of the value function')
+    parser.add_argument('--max-grad-norm', type=float, default=0.5,
+        help='the maximum norm for the gradient clipping')
+    parser.add_argument('--target-kl', type=float, default=None,
+        help='the target KL divergence threshold')
+    args = parser.parse_args()
+    args.batch_size = int(args.num_envs * args.num_steps)
+    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+    return args
+
+if __name__ == "__main__":
+    args = parse_args()
+    print(args)
+    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+    if args.track:
+        import wandb
+
+        wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            sync_tensorboard=True,
+            config=vars(args),
+            name=run_name,
+            monitor_gym=True,
+            save_code=True,
+        )
+    writer =SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
+    )
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
+
+    envs = gym.vector.SyncVectorEnv(
+        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
+    for i in range(args.num_envs)])
+    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
+
+    agent = Agent(envs).to(device)
+    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
+    
+    
+    # ALGO Logic: Storage setup
+    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
+    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
+    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
+
+    #TRY NOT TO MODIFY: start the game
+    global_step = 0
+    start_time =time.time()
+    next_obs = torch.Tensor(envs.reset()).to(device)
+    next_done = torch.zeros(args.num_envs).to(device)
+    num_updates = args.total_timesteps // args.batch_size
+
+    for update in range(1, num_updates+1):
+        # Annealing the rate if instructed to do so.abs
+        if args.anneal_lr:
+            frac = 1.0 -(update -1.0) / num_updates
+            lrnow = frac * args.learning_rate
+            optimizer.param_groups[0]["lr"]=lrnow
+        
+        for step in range(0, args.num_steps):
+            global_step += 1 * args.num_envs
+            obs[step] = next_obs
+            dones[step] = next_done
+
+            # ALGO LOGIC : action logic
+            with torch.no_grad():
+                action, logprob, _, value = agent.get_action_and_value(next_obs)
+                values[step] = value.flatten()
+            actions[step] = action
+            logprobs[step] = logprob
+
+            # TRY NOT TO MODIFY: execute the game and log data.abs
+            next_obs, reward, done, info = envs.step(action.cpu().numpy())
+            rewards[step] = torch.tensor(reward).to(device).view(-1)
+            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
+        
+            if isinstance(info, dict) and "episode" in info:
+                for item in info["episode"]:
+                    if item is not None:
+                        print(f"global_step={global_step}, episodic_return={item['r']}")
+                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
+                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
+                        break
+            
+            #bootstrap reward if not done
+            with torch.no_grad():
+                next_value = agent.get_value(next_obs).reshape(1,-1)
+                if args.gae:
+                    advantages = torch.zeros_like(rewards).to(device)
+                    lastgaelam = 0
+                    for t in reversed(range(args.num_steps)):
+                        if t == args.num_steps - 1:
+                            nextnonterminal = 1.0 - next_done
+                            nextvalues = next_value
+                        else:
+                            nextnonterminal = 1.0 - dones[t+1]
+                            nextvalues = values[t+1]
+                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
+                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
+                    returns =advantages + values
+                else:
+                    returns =torch.zeros_like(rewards).to(device)
+                    for t in reversed(range(args.num_steps)):
+                        if t== args.num_steps - 1:
+                            nextnonterminal = 1.0 -next_done
+                            next_return = next_value
+                        else:
+                            nextnonterminal = 1.0 - dones[t+1]
+                            next_return = returns[t+1]
+                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
+                    advantages = returns - values
+            
+            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
+            b_logprobs =logprobs.reshape(-1)
+            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
+            b_advantages =advantages.reshape(-1)
+            b_returns = returns.reshape(-1)
+            b_values = values.reshape(-1)
+
+            # Optimizaing the policy and value network
+            b_inds = np.arange(args.batch_size)
+            clipfracs = []
+            for epoch in range(args.update_epochs):
+                np.random.shuffle(b_inds)
+                for start in range(0,args.batch_size, args.minibatch_size):
+                    end = start + args.minibatch_size
+                    mb_inds = b_inds[start:end]
+
+                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
+                        b_obs[mb_inds], b_actions.long()[mb_inds]
+                    )
+                    logratio = newlogprob - b_logprobs[mb_inds]
+                    ratio = logratio.exp()
+
+                    with torch.no_grad():
+                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
+                        old_approx_kl = (-logratio).mean()
+                        approx_kl =((ratio-1) - logratio).mean()
+                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
+
+                    mb_advantages = b_advantages[mb_inds]
+                    if args.norm_adv:
+                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
+
+                    # Policy loss
+                    pg_loss1 = -mb_advantages * ratio
+                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
+                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
+
+                    # Value loss
+                    newvalue = newvalue.view(-1)
+                    if args.clip_vloss:
+                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
+                        v_clipped = b_values[mb_inds] + torch.clamp(
+                            newvalue - b_values[mb_inds],
+                            -args.clip_coef,
+                            args.clip_coef
+                        )
+                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
+                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
+                        v_loss = 0.5 * v_loss_max.mean()
+                    else:
+                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
+                    
+                    # Entropy loss
+                    entropy_loss = entropy.mean()
+                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
+
+                    optimizer.zero_grad()
+                    loss.backward()
+                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
+                    optimizer.step()
+            
+                if args.target_kl is not None:
+                    if approx_kl > args.target_kl:
+                        break
+
+        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
+        var_y = np.var(y_true)
+        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
+
+        # TRY NOT TO MODIFY: record rewards for plotting purposes
+        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
+        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
+        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
+        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
+        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
+        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
+        writer.add_scalar("losses/explained_variance",explained_var, global_step)
+        print("SPS:", int(global_step / (time.time() - start_time)))
+        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
+
+    envs.close()
+    writer.close()
\ No newline at end of file
diff --git a/wandb/run-20260421_155543-quaracjv/files/config.yaml b/wandb/run-20260421_155543-quaracjv/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c6706894b88c79782c6a4ede99163a56e4b79779
--- /dev/null
+++ b/wandb/run-20260421_155543-quaracjv/files/config.yaml
@@ -0,0 +1,102 @@
+_wandb:
+    value:
+        cli_version: 0.26.0
+        code_path: code/ppo.py
+        e:
+            bp1188b62nh6vd31amfonzgm7c33xmvg:
+                args:
+                    - --track
+                    - --capture-video
+                codePath: ppo.py
+                codePathLocal: ppo.py
+                cpu_count: 12
+                cpu_count_logical: 14
+                disk:
+                    /:
+                        total: "499680116736"
+                        used: "141826818048"
+                email: loisfernandezcmp@gmail.com
+                executable: /home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/bin/python
+                git:
+                    commit: e1dc52c8934ce180fef18fbdf53a72281f134a52
+                    remote: https://github.com/loisonchambers/Unit-8-RL-course.git
+                host: BL-LFERNANDEZ.local.eurecat.org
+                memory:
+                    total: "33076023296"
+                os: Linux-6.17.0-20-generic-x86_64-with-glibc2.39
+                program: /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py
+                python: CPython 3.9.5
+                root: /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8
+                startedAt: "2026-04-21T13:55:43.739027Z"
+                writerId: bp1188b62nh6vd31amfonzgm7c33xmvg
+        m: []
+        python_version: 3.9.5
+        t:
+            "1":
+                - 1
+            "2":
+                - 1
+            "3":
+                - 13
+                - 16
+                - 35
+            "4": 3.9.5
+            "5": 0.26.0
+            "12": 0.26.0
+            "13": linux-x86_64
+anneal_lr:
+    value: true
+batch_size:
+    value: 512
+capture_video:
+    value: true
+clip_coef:
+    value: 0.2
+clip_vloss:
+    value: true
+cuda:
+    value: true
+ent_coef:
+    value: 0.01
+exp_name:
+    value: ppo
+gae:
+    value: true
+gae_lambda:
+    value: 0.95
+gamma:
+    value: 0.99
+gym_id:
+    value: CartPole-v1
+learning_rate:
+    value: 0.00025
+max_grad_norm:
+    value: 0.5
+minibatch_size:
+    value: 128
+norm_adv:
+    value: true
+num_envs:
+    value: 4
+num_minibatches:
+    value: 4
+num_steps:
+    value: 128
+seed:
+    value: 1
+target_kl:
+    value: null
+torch_deterministic:
+    value: true
+total_timesteps:
+    value: 25000
+track:
+    value: true
+update_epochs:
+    value: 4
+vf_coef:
+    value: 0.5
+wandb_entity:
+    value: null
+wandb_project_name:
+    value: cleanRL
diff --git a/wandb/run-20260421_155543-quaracjv/files/diff.patch b/wandb/run-20260421_155543-quaracjv/files/diff.patch
new file mode 100644
index 0000000000000000000000000000000000000000..d17cd83238939e160b32c123d97d1823c21c2c56
--- /dev/null
+++ b/wandb/run-20260421_155543-quaracjv/files/diff.patch
@@ -0,0 +1,1316 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..b2a42e5 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
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+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
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++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[[package]]
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++version = "3.4.7"
++description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
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++python-versions = ">=3.7"
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++]
++
++[[package]]
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++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++smmap = ">=3.0.1,<6"
++
++[[package]]
++name = "gitpython"
++version = "3.1.46"
++description = "GitPython is a Python library used to interact with Git repositories"
++optional = false
++python-versions = ">=3.7"
++files = [
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++    {file = "gitpython-3.1.46.tar.gz", hash = "sha256:400124c7d0ef4ea03f7310ac2fbf7151e09ff97f2a3288d64a440c584a29c37f"},
++]
++
++[package.dependencies]
++gitdb = ">=4.0.1,<5"
++typing-extensions = {version = ">=3.10.0.2", markers = "python_version < \"3.10\""}
++
++[package.extras]
++doc = ["sphinx (>=7.1.2,<7.2)", "sphinx-autodoc-typehints", "sphinx_rtd_theme"]
++test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy (==1.18.2)", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
++
+ [[package]]
+ name = "grpcio"
+ version = "1.80.0"
+@@ -303,6 +524,45 @@ typing-extensions = ">=4.12,<5.0"
+ [package.extras]
+ protobuf = ["grpcio-tools (>=1.80.0)"]
+ 
++[[package]]
++name = "gym"
++version = "0.26.2"
++description = "Gym: A universal API for reinforcement learning environments"
++optional = false
++python-versions = ">=3.6"
++files = [
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++]
++
++[package.dependencies]
++cloudpickle = ">=1.2.0"
++gym_notices = ">=0.0.4"
++importlib_metadata = {version = ">=4.8.0", markers = "python_version < \"3.10\""}
++numpy = ">=1.18.0"
++
++[package.extras]
++accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"]
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++atari = ["ale-py (>=0.8.0,<0.9.0)"]
++box2d = ["box2d-py (==2.3.5)", "pygame (==2.1.0)", "swig (==4.*)"]
++classic-control = ["pygame (==2.1.0)"]
++mujoco = ["imageio (>=2.14.1)", "mujoco (==2.2)"]
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++
++[[package]]
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+ 
++[[package]]
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+@@ -919,6 +1192,22 @@ tests = ["check-manifest", "coverage (>=7.4.2)", "defusedxml", "markdown2", "ole
+ typing = ["typing-extensions"]
+ xmp = ["defusedxml"]
+ 
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+-cover = ["pytest-cov"]
+-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+-enabler = ["pytest-enabler (>=2.2)"]
+-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+-type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.18.*)", "pytest-mypy"]
++socks = ["PySocks (>=1.5.6,!=1.5.7)"]
++use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
++
++[[package]]
++name = "sentry-sdk"
++version = "2.58.0"
++description = "Python client for Sentry (https://sentry.io)"
++optional = false
++python-versions = ">=3.6"
++files = [
++    {file = "sentry_sdk-2.58.0-py2.py3-none-any.whl", hash = "sha256:688d1c704ddecf382ea3326f21a67453d4caa95592d722b7c780a36a9d23109e"},
++    {file = "sentry_sdk-2.58.0.tar.gz", hash = "sha256:c1144d947352d54e5b7daa63596d9f848adf684989c06c4f5a659f0c85a18f6f"},
++]
++
++[package.dependencies]
++certifi = "*"
++urllib3 = ">=1.26.11"
++
++[package.extras]
++aiohttp = ["aiohttp (>=3.5)"]
++anthropic = ["anthropic (>=0.16)"]
++arq = ["arq (>=0.23)"]
++asyncio = ["httpcore[asyncio] (==1.*)"]
++asyncpg = ["asyncpg (>=0.23)"]
++beam = ["apache-beam (>=2.12)"]
++bottle = ["bottle (>=0.12.13)"]
++celery = ["celery (>=3)"]
++celery-redbeat = ["celery-redbeat (>=2)"]
++chalice = ["chalice (>=1.16.0)"]
++clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
++django = ["django (>=1.8)"]
++falcon = ["falcon (>=1.4)"]
++fastapi = ["fastapi (>=0.79.0)"]
++flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
++google-genai = ["google-genai (>=1.29.0)"]
++grpcio = ["grpcio (>=1.21.1)", "protobuf (>=3.8.0)"]
++http2 = ["httpcore[http2] (==1.*)"]
++httpx = ["httpx (>=0.16.0)"]
++huey = ["huey (>=2)"]
++huggingface-hub = ["huggingface_hub (>=0.22)"]
++langchain = ["langchain (>=0.0.210)"]
++langgraph = ["langgraph (>=0.6.6)"]
++launchdarkly = ["launchdarkly-server-sdk (>=9.8.0)"]
++litellm = ["litellm (>=1.77.5,!=1.82.7,!=1.82.8)"]
++litestar = ["litestar (>=2.0.0)"]
++loguru = ["loguru (>=0.5)"]
++mcp = ["mcp (>=1.15.0)"]
++openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
++openfeature = ["openfeature-sdk (>=0.7.1)"]
++opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
++opentelemetry-experimental = ["opentelemetry-distro"]
++opentelemetry-otlp = ["opentelemetry-distro[otlp] (>=0.35b0)"]
++pure-eval = ["asttokens", "executing", "pure_eval"]
++pydantic-ai = ["pydantic-ai (>=1.0.0)"]
++pymongo = ["pymongo (>=3.1)"]
++pyspark = ["pyspark (>=2.4.4)"]
++quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
++rq = ["rq (>=0.6)"]
++sanic = ["sanic (>=0.8)"]
++sqlalchemy = ["sqlalchemy (>=1.2)"]
++starlette = ["starlette (>=0.19.1)"]
++starlite = ["starlite (>=1.48)"]
++statsig = ["statsig (>=0.55.3)"]
++tornado = ["tornado (>=6)"]
++unleash = ["UnleashClient (>=6.0.1)"]
++
++[[package]]
++name = "setuptools"
++version = "65.5.0"
++description = "Easily download, build, install, upgrade, and uninstall Python packages"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "setuptools-65.5.0-py3-none-any.whl", hash = "sha256:f62ea9da9ed6289bfe868cd6845968a2c854d1427f8548d52cae02a42b4f0356"},
++    {file = "setuptools-65.5.0.tar.gz", hash = "sha256:512e5536220e38146176efb833d4a62aa726b7bbff82cfbc8ba9eaa3996e0b17"},
++]
++
++[package.extras]
++docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
++testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
++testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
++
++[[package]]
++name = "smmap"
++version = "5.0.3"
++description = "A pure Python implementation of a sliding window memory map manager"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "smmap-5.0.3-py3-none-any.whl", hash = "sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f"},
++    {file = "smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c"},
++]
+ 
+ [[package]]
+ name = "sympy"
+@@ -1197,6 +1881,81 @@ files = [
+     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+ ]
+ 
++[[package]]
++name = "typing-inspection"
++version = "0.4.2"
++description = "Runtime typing introspection tools"
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
++    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
++]
++
++[package.dependencies]
++typing-extensions = ">=4.12.0"
++
++[[package]]
++name = "urllib3"
++version = "2.6.3"
++description = "HTTP library with thread-safe connection pooling, file post, and more."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
++brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"]
++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:1ece94a2a5eda1d0e3a2d8a2fd28aa0187705d6efa5ac4c0b8680083583b7ec1"},
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:92f6f303fe2af50e3f711833a835150f9b4d8082874bfd9868cf15491ea2947e"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:a17aae051a31831388cff880251c1b5bc38fbf6a283a0ee7c543709e8e9633d1"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:28a14ffc014e523498d077dfde12839b7be586ca8c3190e72e7167c1aea6ee4c"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1995,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "f05e4899a94176c110f2df2a85ded5c10bb7d8072f071a35c990cfb2d15b58f0"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..29673f1 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -2,8 +2,62 @@ import argparse
+ import os
+ from distutils.util import strtobool
+ import time
++import random
++import numpy as np
++import gym
++import torch
++import torch.nn as nn
++import torch.optim as optim
++from torch.distributions.categorical import Categorical
+ from torch.utils.tensorboard import SummaryWriter
+ 
++def make_env(gym_id, seed, idx, capture_video, run_name):
++    def thunk():
++        env = gym.make(gym_id)
++        env = gym.wrappers.RecordEpisodeStatistics(env)
++        if capture_video:
++            if idx == 0:
++                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", record_video_trigger=lambda t: t % 1000 == 0)
++        env.seed(seed)
++        env.action_space.seed(seed)
++        env.observation_space.seed(seed)
++        return env
++    return thunk
++
++def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
++    torch.nn.init.orthogonal_(layer.weight, std)
++    torch.nn.init.constant_(layer.bias, bias_const)
++    return layer
++
++class Agent(nn.Module):
++    def __init__(self, envs):
++        super(Agent, self).__init__()
++        self.critic = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,1), std=1.),
++        )
++
++        self.actor = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
++        )
++
++    def get_value(self, x):
++        return self.critic(x)
++    
++    def get_action_and_value(self, x, action=None):
++        logits = self.actor(x)
++        probs = Categorical(logits=logits)
++        if action is None:
++            action = probs.sample()
++        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
++
+ def parse_args():
+     parser = argparse.ArgumentParser()
+     parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+@@ -16,21 +70,244 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
++    parser.add_argument("--num-envs", type=int, default=4,
++    help="the number of parallel game environments")
++    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
++        help="whether to capture videos of the agent performances (check out `videos` folder)")
++    parser.add_argument("--num-steps", type=int, default=128,
++        help="the number of steps to run in each environment per policy rollout")
++    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggle learning rate annealing for policy and value networks")  
++    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Use GAE for advantage computation")  
++    parser.add_argument('--gamma', type=float, default=0.99,
++        help='the discount factor gamma')
++    parser.add_argument('--gae-lambda', type=float, default=0.95,
++        help='the lambda for the general advantage estimation')
++    parser.add_argument("--num-minibatches", type=int, default=4,
++        help="the number of mini-batches")
++    parser.add_argument("--update-epochs", type=int, default=4,
++        help="the K epochs to update the policy")
++    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles advantages normalization")
++    parser.add_argument('--clip-coef', type=float, default=0.2,
++        help='the surrogate clipping coefficient')     
++    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
++    parser.add_argument('--ent-coef', type=float, default=0.01,
++        help='coefficient of the entropy')
++    parser.add_argument('--vf-coef', type=float, default=0.5,
++        help='coefficient of the value function')
++    parser.add_argument('--max-grad-norm', type=float, default=0.5,
++        help='the maximum norm for the gradient clipping')
++    parser.add_argument('--target-kl', type=float, default=None,
++        help='the target KL divergence threshold')
+     args = parser.parse_args()
++    args.batch_size = int(args.num_envs * args.num_steps)
++    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    envs = gym.vector.SyncVectorEnv(
++        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
++    for i in range(args.num_envs)])
++    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
++
++    agent = Agent(envs).to(device)
++    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
++    
++    
++    # ALGO Logic: Storage setup
++    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
++    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
++    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
++
++    #TRY NOT TO MODIFY: start the game
++    global_step = 0
++    start_time =time.time()
++    next_obs = torch.Tensor(envs.reset()).to(device)
++    next_done = torch.zeros(args.num_envs).to(device)
++    num_updates = args.total_timesteps // args.batch_size
++
++    for update in range(1, num_updates+1):
++        # Annealing the rate if instructed to do so.abs
++        if args.anneal_lr:
++            frac = 1.0 -(update -1.0) / num_updates
++            lrnow = frac * args.learning_rate
++            optimizer.param_groups[0]["lr"]=lrnow
++        
++        for step in range(0, args.num_steps):
++            global_step += 1 * args.num_envs
++            obs[step] = next_obs
++            dones[step] = next_done
++
++            # ALGO LOGIC : action logic
++            with torch.no_grad():
++                action, logprob, _, value = agent.get_action_and_value(next_obs)
++                values[step] = value.flatten()
++            actions[step] = action
++            logprobs[step] = logprob
++
++            # TRY NOT TO MODIFY: execute the game and log data.abs
++            next_obs, reward, done, info = envs.step(action.cpu().numpy())
++            rewards[step] = torch.tensor(reward).to(device).view(-1)
++            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
++        
++            if isinstance(info, dict) and "episode" in info:
++                for item in info["episode"]:
++                    if item is not None:
++                        print(f"global_step={global_step}, episodic_return={item['r']}")
++                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
++                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
++                        break
++            
++            #bootstrap reward if not done
++            with torch.no_grad():
++                next_value = agent.get_value(next_obs).reshape(1,-1)
++                if args.gae:
++                    advantages = torch.zeros_like(rewards).to(device)
++                    lastgaelam = 0
++                    for t in reversed(range(args.num_steps)):
++                        if t == args.num_steps - 1:
++                            nextnonterminal = 1.0 - next_done
++                            nextvalues = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            nextvalues = values[t+1]
++                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
++                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
++                    returns =advantages + values
++                else:
++                    returns =torch.zeros_like(rewards).to(device)
++                    for t in reversed(range(args.num_steps)):
++                        if t== args.num_steps - 1:
++                            nextnonterminal = 1.0 -next_done
++                            next_return = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            next_return = returns[t+1]
++                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
++                    advantages = returns - values
++            
++            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
++            b_logprobs =logprobs.reshape(-1)
++            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
++            b_advantages =advantages.reshape(-1)
++            b_returns = returns.reshape(-1)
++            b_values = values.reshape(-1)
++
++            # Optimizaing the policy and value network
++            b_inds = np.arange(args.batch_size)
++            clipfracs = []
++            for epoch in range(args.update_epochs):
++                np.random.shuffle(b_inds)
++                for start in range(0,args.batch_size, args.minibatch_size):
++                    end = start + args.minibatch_size
++                    mb_inds = b_inds[start:end]
++
++                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
++                        b_obs[mb_inds], b_actions.long()[mb_inds]
++                    )
++                    logratio = newlogprob - b_logprobs[mb_inds]
++                    ratio = logratio.exp()
++
++                    with torch.no_grad():
++                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
++                        old_approx_kl = (-logratio).mean()
++                        approx_kl =((ratio-1) - logratio).mean()
++                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
++
++                    mb_advantages = b_advantages[mb_inds]
++                    if args.norm_adv:
++                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
++
++                    # Policy loss
++                    pg_loss1 = -mb_advantages * ratio
++                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
++                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
++
++                    # Value loss
++                    newvalue = newvalue.view(-1)
++                    if args.clip_vloss:
++                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
++                        v_clipped = b_values[mb_inds] + torch.clamp(
++                            newvalue - b_values[mb_inds],
++                            -args.clip_coef,
++                            args.clip_coef
++                        )
++                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
++                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
++                        v_loss = 0.5 * v_loss_max.mean()
++                    else:
++                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
++                    
++                    # Entropy loss
++                    entropy_loss = entropy.mean()
++                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
++
++                    optimizer.zero_grad()
++                    loss.backward()
++                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
++                    optimizer.step()
++            
++                if args.target_kl is not None:
++                    if approx_kl > args.target_kl:
++                        break
++
++        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
++        var_y = np.var(y_true)
++        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
++
++        # TRY NOT TO MODIFY: record rewards for plotting purposes
++        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
++        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
++        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
++        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
++        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
++        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
++        writer.add_scalar("losses/explained_variance",explained_var, global_step)
++        print("SPS:", int(global_step / (time.time() - start_time)))
++        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
++
++    envs.close()
++    writer.close()
+\ No newline at end of file
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..b618832 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.26.2"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_155543-quaracjv/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch b/wandb/run-20260421_155543-quaracjv/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
new file mode 100644
index 0000000000000000000000000000000000000000..d17cd83238939e160b32c123d97d1823c21c2c56
--- /dev/null
+++ b/wandb/run-20260421_155543-quaracjv/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
@@ -0,0 +1,1316 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..b2a42e5 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
++    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"},
++    {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"},
++]
++
++[[package]]
++name = "charset-normalizer"
++version = "3.4.7"
++description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "charset_normalizer-3.4.7-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:cdd68a1fb318e290a2077696b7eb7a21a49163c455979c639bf5a5dcdc46617d"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e17b8d5d6a8c47c85e68ca8379def1303fd360c3e22093a807cd34a71cd082b8"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:511ef87c8aec0783e08ac18565a16d435372bc1ac25a91e6ac7f5ef2b0bff790"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:007d05ec7321d12a40227aae9e2bc6dca73f3cb21058999a1df9e193555a9dcc"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:cf29836da5119f3c8a8a70667b0ef5fdca3bb12f80fd06487cfa575b3909b393"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux_2_31_armv7l.whl", hash = "sha256:12d8baf840cc7889b37c7c770f478adea7adce3dcb3944d02ec87508e2dcf153"},
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++]
++
++[[package]]
++name = "click"
++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
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++python-versions = ">=3.7"
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+ 
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+ xmp = ["defusedxml"]
+ 
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++    {file = "pyyaml-6.0.3-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3c5677e12444c15717b902a5798264fa7909e41153cdf9ef7ad571b704a63dd9"},
++    {file = "pyyaml-6.0.3-cp39-cp39-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5ed875a24292240029e4483f9d4a4b8a1ae08843b9c54f43fcc11e404532a8a5"},
++    {file = "pyyaml-6.0.3-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0150219816b6a1fa26fb4699fb7daa9caf09eb1999f3b70fb6e786805e80375a"},
++    {file = "pyyaml-6.0.3-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:fa160448684b4e94d80416c0fa4aac48967a969efe22931448d853ada8baf926"},
++    {file = "pyyaml-6.0.3-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:27c0abcb4a5dac13684a37f76e701e054692a9b2d3064b70f5e4eb54810553d7"},
++    {file = "pyyaml-6.0.3-cp39-cp39-win32.whl", hash = "sha256:1ebe39cb5fc479422b83de611d14e2c0d3bb2a18bbcb01f229ab3cfbd8fee7a0"},
++    {file = "pyyaml-6.0.3-cp39-cp39-win_amd64.whl", hash = "sha256:2e71d11abed7344e42a8849600193d15b6def118602c4c176f748e4583246007"},
++    {file = "pyyaml-6.0.3.tar.gz", hash = "sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f"},
++]
++
++[[package]]
++name = "requests"
++version = "2.32.5"
++description = "Python HTTP for Humans."
+ optional = false
+ python-versions = ">=3.9"
+ files = [
+-    {file = "setuptools-82.0.1-py3-none-any.whl", hash = "sha256:a59e362652f08dcd477c78bb6e7bd9d80a7995bc73ce773050228a348ce2e5bb"},
+-    {file = "setuptools-82.0.1.tar.gz", hash = "sha256:7d872682c5d01cfde07da7bccc7b65469d3dca203318515ada1de5eda35efbf9"},
++    {file = "requests-2.32.5-py3-none-any.whl", hash = "sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6"},
++    {file = "requests-2.32.5.tar.gz", hash = "sha256:dbba0bac56e100853db0ea71b82b4dfd5fe2bf6d3754a8893c3af500cec7d7cf"},
+ ]
+ 
++[package.dependencies]
++certifi = ">=2017.4.17"
++charset_normalizer = ">=2,<4"
++idna = ">=2.5,<4"
++urllib3 = ">=1.21.1,<3"
++
+ [package.extras]
+-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.13.0)"]
+-core = ["importlib_metadata (>=6)", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more_itertools", "more_itertools (>=8.8)", "packaging (>=24.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
+-cover = ["pytest-cov"]
+-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+-enabler = ["pytest-enabler (>=2.2)"]
+-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+-type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.18.*)", "pytest-mypy"]
++socks = ["PySocks (>=1.5.6,!=1.5.7)"]
++use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
++
++[[package]]
++name = "sentry-sdk"
++version = "2.58.0"
++description = "Python client for Sentry (https://sentry.io)"
++optional = false
++python-versions = ">=3.6"
++files = [
++    {file = "sentry_sdk-2.58.0-py2.py3-none-any.whl", hash = "sha256:688d1c704ddecf382ea3326f21a67453d4caa95592d722b7c780a36a9d23109e"},
++    {file = "sentry_sdk-2.58.0.tar.gz", hash = "sha256:c1144d947352d54e5b7daa63596d9f848adf684989c06c4f5a659f0c85a18f6f"},
++]
++
++[package.dependencies]
++certifi = "*"
++urllib3 = ">=1.26.11"
++
++[package.extras]
++aiohttp = ["aiohttp (>=3.5)"]
++anthropic = ["anthropic (>=0.16)"]
++arq = ["arq (>=0.23)"]
++asyncio = ["httpcore[asyncio] (==1.*)"]
++asyncpg = ["asyncpg (>=0.23)"]
++beam = ["apache-beam (>=2.12)"]
++bottle = ["bottle (>=0.12.13)"]
++celery = ["celery (>=3)"]
++celery-redbeat = ["celery-redbeat (>=2)"]
++chalice = ["chalice (>=1.16.0)"]
++clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
++django = ["django (>=1.8)"]
++falcon = ["falcon (>=1.4)"]
++fastapi = ["fastapi (>=0.79.0)"]
++flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
++google-genai = ["google-genai (>=1.29.0)"]
++grpcio = ["grpcio (>=1.21.1)", "protobuf (>=3.8.0)"]
++http2 = ["httpcore[http2] (==1.*)"]
++httpx = ["httpx (>=0.16.0)"]
++huey = ["huey (>=2)"]
++huggingface-hub = ["huggingface_hub (>=0.22)"]
++langchain = ["langchain (>=0.0.210)"]
++langgraph = ["langgraph (>=0.6.6)"]
++launchdarkly = ["launchdarkly-server-sdk (>=9.8.0)"]
++litellm = ["litellm (>=1.77.5,!=1.82.7,!=1.82.8)"]
++litestar = ["litestar (>=2.0.0)"]
++loguru = ["loguru (>=0.5)"]
++mcp = ["mcp (>=1.15.0)"]
++openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
++openfeature = ["openfeature-sdk (>=0.7.1)"]
++opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
++opentelemetry-experimental = ["opentelemetry-distro"]
++opentelemetry-otlp = ["opentelemetry-distro[otlp] (>=0.35b0)"]
++pure-eval = ["asttokens", "executing", "pure_eval"]
++pydantic-ai = ["pydantic-ai (>=1.0.0)"]
++pymongo = ["pymongo (>=3.1)"]
++pyspark = ["pyspark (>=2.4.4)"]
++quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
++rq = ["rq (>=0.6)"]
++sanic = ["sanic (>=0.8)"]
++sqlalchemy = ["sqlalchemy (>=1.2)"]
++starlette = ["starlette (>=0.19.1)"]
++starlite = ["starlite (>=1.48)"]
++statsig = ["statsig (>=0.55.3)"]
++tornado = ["tornado (>=6)"]
++unleash = ["UnleashClient (>=6.0.1)"]
++
++[[package]]
++name = "setuptools"
++version = "65.5.0"
++description = "Easily download, build, install, upgrade, and uninstall Python packages"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "setuptools-65.5.0-py3-none-any.whl", hash = "sha256:f62ea9da9ed6289bfe868cd6845968a2c854d1427f8548d52cae02a42b4f0356"},
++    {file = "setuptools-65.5.0.tar.gz", hash = "sha256:512e5536220e38146176efb833d4a62aa726b7bbff82cfbc8ba9eaa3996e0b17"},
++]
++
++[package.extras]
++docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
++testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
++testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
++
++[[package]]
++name = "smmap"
++version = "5.0.3"
++description = "A pure Python implementation of a sliding window memory map manager"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "smmap-5.0.3-py3-none-any.whl", hash = "sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f"},
++    {file = "smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c"},
++]
+ 
+ [[package]]
+ name = "sympy"
+@@ -1197,6 +1881,81 @@ files = [
+     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+ ]
+ 
++[[package]]
++name = "typing-inspection"
++version = "0.4.2"
++description = "Runtime typing introspection tools"
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
++    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
++]
++
++[package.dependencies]
++typing-extensions = ">=4.12.0"
++
++[[package]]
++name = "urllib3"
++version = "2.6.3"
++description = "HTTP library with thread-safe connection pooling, file post, and more."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
++brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"]
++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:1ece94a2a5eda1d0e3a2d8a2fd28aa0187705d6efa5ac4c0b8680083583b7ec1"},
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:92f6f303fe2af50e3f711833a835150f9b4d8082874bfd9868cf15491ea2947e"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:a17aae051a31831388cff880251c1b5bc38fbf6a283a0ee7c543709e8e9633d1"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:28a14ffc014e523498d077dfde12839b7be586ca8c3190e72e7167c1aea6ee4c"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1995,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "f05e4899a94176c110f2df2a85ded5c10bb7d8072f071a35c990cfb2d15b58f0"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..29673f1 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -2,8 +2,62 @@ import argparse
+ import os
+ from distutils.util import strtobool
+ import time
++import random
++import numpy as np
++import gym
++import torch
++import torch.nn as nn
++import torch.optim as optim
++from torch.distributions.categorical import Categorical
+ from torch.utils.tensorboard import SummaryWriter
+ 
++def make_env(gym_id, seed, idx, capture_video, run_name):
++    def thunk():
++        env = gym.make(gym_id)
++        env = gym.wrappers.RecordEpisodeStatistics(env)
++        if capture_video:
++            if idx == 0:
++                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", record_video_trigger=lambda t: t % 1000 == 0)
++        env.seed(seed)
++        env.action_space.seed(seed)
++        env.observation_space.seed(seed)
++        return env
++    return thunk
++
++def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
++    torch.nn.init.orthogonal_(layer.weight, std)
++    torch.nn.init.constant_(layer.bias, bias_const)
++    return layer
++
++class Agent(nn.Module):
++    def __init__(self, envs):
++        super(Agent, self).__init__()
++        self.critic = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,1), std=1.),
++        )
++
++        self.actor = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
++        )
++
++    def get_value(self, x):
++        return self.critic(x)
++    
++    def get_action_and_value(self, x, action=None):
++        logits = self.actor(x)
++        probs = Categorical(logits=logits)
++        if action is None:
++            action = probs.sample()
++        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
++
+ def parse_args():
+     parser = argparse.ArgumentParser()
+     parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+@@ -16,21 +70,244 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
++    parser.add_argument("--num-envs", type=int, default=4,
++    help="the number of parallel game environments")
++    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
++        help="whether to capture videos of the agent performances (check out `videos` folder)")
++    parser.add_argument("--num-steps", type=int, default=128,
++        help="the number of steps to run in each environment per policy rollout")
++    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggle learning rate annealing for policy and value networks")  
++    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Use GAE for advantage computation")  
++    parser.add_argument('--gamma', type=float, default=0.99,
++        help='the discount factor gamma')
++    parser.add_argument('--gae-lambda', type=float, default=0.95,
++        help='the lambda for the general advantage estimation')
++    parser.add_argument("--num-minibatches", type=int, default=4,
++        help="the number of mini-batches")
++    parser.add_argument("--update-epochs", type=int, default=4,
++        help="the K epochs to update the policy")
++    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles advantages normalization")
++    parser.add_argument('--clip-coef', type=float, default=0.2,
++        help='the surrogate clipping coefficient')     
++    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
++    parser.add_argument('--ent-coef', type=float, default=0.01,
++        help='coefficient of the entropy')
++    parser.add_argument('--vf-coef', type=float, default=0.5,
++        help='coefficient of the value function')
++    parser.add_argument('--max-grad-norm', type=float, default=0.5,
++        help='the maximum norm for the gradient clipping')
++    parser.add_argument('--target-kl', type=float, default=None,
++        help='the target KL divergence threshold')
+     args = parser.parse_args()
++    args.batch_size = int(args.num_envs * args.num_steps)
++    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    envs = gym.vector.SyncVectorEnv(
++        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
++    for i in range(args.num_envs)])
++    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
++
++    agent = Agent(envs).to(device)
++    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
++    
++    
++    # ALGO Logic: Storage setup
++    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
++    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
++    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
++
++    #TRY NOT TO MODIFY: start the game
++    global_step = 0
++    start_time =time.time()
++    next_obs = torch.Tensor(envs.reset()).to(device)
++    next_done = torch.zeros(args.num_envs).to(device)
++    num_updates = args.total_timesteps // args.batch_size
++
++    for update in range(1, num_updates+1):
++        # Annealing the rate if instructed to do so.abs
++        if args.anneal_lr:
++            frac = 1.0 -(update -1.0) / num_updates
++            lrnow = frac * args.learning_rate
++            optimizer.param_groups[0]["lr"]=lrnow
++        
++        for step in range(0, args.num_steps):
++            global_step += 1 * args.num_envs
++            obs[step] = next_obs
++            dones[step] = next_done
++
++            # ALGO LOGIC : action logic
++            with torch.no_grad():
++                action, logprob, _, value = agent.get_action_and_value(next_obs)
++                values[step] = value.flatten()
++            actions[step] = action
++            logprobs[step] = logprob
++
++            # TRY NOT TO MODIFY: execute the game and log data.abs
++            next_obs, reward, done, info = envs.step(action.cpu().numpy())
++            rewards[step] = torch.tensor(reward).to(device).view(-1)
++            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
++        
++            if isinstance(info, dict) and "episode" in info:
++                for item in info["episode"]:
++                    if item is not None:
++                        print(f"global_step={global_step}, episodic_return={item['r']}")
++                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
++                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
++                        break
++            
++            #bootstrap reward if not done
++            with torch.no_grad():
++                next_value = agent.get_value(next_obs).reshape(1,-1)
++                if args.gae:
++                    advantages = torch.zeros_like(rewards).to(device)
++                    lastgaelam = 0
++                    for t in reversed(range(args.num_steps)):
++                        if t == args.num_steps - 1:
++                            nextnonterminal = 1.0 - next_done
++                            nextvalues = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            nextvalues = values[t+1]
++                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
++                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
++                    returns =advantages + values
++                else:
++                    returns =torch.zeros_like(rewards).to(device)
++                    for t in reversed(range(args.num_steps)):
++                        if t== args.num_steps - 1:
++                            nextnonterminal = 1.0 -next_done
++                            next_return = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            next_return = returns[t+1]
++                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
++                    advantages = returns - values
++            
++            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
++            b_logprobs =logprobs.reshape(-1)
++            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
++            b_advantages =advantages.reshape(-1)
++            b_returns = returns.reshape(-1)
++            b_values = values.reshape(-1)
++
++            # Optimizaing the policy and value network
++            b_inds = np.arange(args.batch_size)
++            clipfracs = []
++            for epoch in range(args.update_epochs):
++                np.random.shuffle(b_inds)
++                for start in range(0,args.batch_size, args.minibatch_size):
++                    end = start + args.minibatch_size
++                    mb_inds = b_inds[start:end]
++
++                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
++                        b_obs[mb_inds], b_actions.long()[mb_inds]
++                    )
++                    logratio = newlogprob - b_logprobs[mb_inds]
++                    ratio = logratio.exp()
++
++                    with torch.no_grad():
++                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
++                        old_approx_kl = (-logratio).mean()
++                        approx_kl =((ratio-1) - logratio).mean()
++                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
++
++                    mb_advantages = b_advantages[mb_inds]
++                    if args.norm_adv:
++                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
++
++                    # Policy loss
++                    pg_loss1 = -mb_advantages * ratio
++                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
++                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
++
++                    # Value loss
++                    newvalue = newvalue.view(-1)
++                    if args.clip_vloss:
++                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
++                        v_clipped = b_values[mb_inds] + torch.clamp(
++                            newvalue - b_values[mb_inds],
++                            -args.clip_coef,
++                            args.clip_coef
++                        )
++                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
++                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
++                        v_loss = 0.5 * v_loss_max.mean()
++                    else:
++                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
++                    
++                    # Entropy loss
++                    entropy_loss = entropy.mean()
++                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
++
++                    optimizer.zero_grad()
++                    loss.backward()
++                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
++                    optimizer.step()
++            
++                if args.target_kl is not None:
++                    if approx_kl > args.target_kl:
++                        break
++
++        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
++        var_y = np.var(y_true)
++        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
++
++        # TRY NOT TO MODIFY: record rewards for plotting purposes
++        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
++        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
++        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
++        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
++        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
++        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
++        writer.add_scalar("losses/explained_variance",explained_var, global_step)
++        print("SPS:", int(global_step / (time.time() - start_time)))
++        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
++
++    envs.close()
++    writer.close()
+\ No newline at end of file
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..b618832 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.26.2"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_155543-quaracjv/files/output.log b/wandb/run-20260421_155543-quaracjv/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..0a648f9ff273b198b0adecbbf8cbbe1378f15911
--- /dev/null
+++ b/wandb/run-20260421_155543-quaracjv/files/output.log
@@ -0,0 +1,26 @@
+Exception ignored in: <function RecordVideo.__del__ at 0x77d87711ed30>
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/record_video.py", line 211, in __del__
+    self.close_video_recorder()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/record_video.py", line 178, in close_video_recorder
+    if self.recording:
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py", line 241, in __getattr__
+    return getattr(self.env, name)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py", line 241, in __getattr__
+    return getattr(self.env, name)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py", line 241, in __getattr__
+    return getattr(self.env, name)
+  [Previous line repeated 988 more times]
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py", line 239, in __getattr__
+    if name.startswith("_"):
+RecursionError: maximum recursion depth exceeded while calling a Python object
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py", line 148, in <module>
+    envs = gym.vector.SyncVectorEnv(
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/vector/sync_vector_env.py", line 52, in __init__
+    self.envs = [env_fn() for env_fn in env_fns]
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/vector/sync_vector_env.py", line 52, in <listcomp>
+    self.envs = [env_fn() for env_fn in env_fns]
+  File "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py", line 20, in thunk
+    env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", record_video_trigger=lambda t: t % 1000 == 0)
+TypeError: __init__() got an unexpected keyword argument 'record_video_trigger'
diff --git a/wandb/run-20260421_155543-quaracjv/files/requirements.txt b/wandb/run-20260421_155543-quaracjv/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a18727d8c95d796af1d287f4f34c504091fea6f
--- /dev/null
+++ b/wandb/run-20260421_155543-quaracjv/files/requirements.txt
@@ -0,0 +1,70 @@
+mpmath==1.3.0
+nvidia-nvtx-cu12==12.6.77
+pluggy==1.6.0
+gym==0.26.2
+charset-normalizer==3.4.7
+Markdown==3.9
+protobuf==6.33.6
+Werkzeug==3.1.8
+gym-notices==0.1.0
+tomli==2.4.1
+GitPython==3.1.46
+nvidia-cuda-cupti-cu12==12.6.80
+MarkupSafe==3.0.3
+certifi==2026.2.25
+nvidia-cublas-cu12==12.6.4.1
+nvidia-cusparselt-cu12==0.6.3
+networkx==3.2.1
+wandb==0.26.0
+tensorboard==2.20.0
+setuptools==65.5.0
+nvidia-cusparse-cu12==12.5.4.2
+gitdb==4.0.12
+pip==24.0
+Jinja2==3.1.6
+pydantic==2.13.3
+coverage==7.10.7
+sentry-sdk==2.58.0
+exceptiongroup==1.3.1
+sympy==1.14.0
+annotated-types==0.7.0
+absl-py==2.3.1
+smmap==5.0.3
+importlib_metadata==8.7.1
+idna==3.11
+pillow==11.3.0
+tensorboard-data-server==0.7.2
+cloudpickle==3.1.2
+platformdirs==4.4.0
+pytest==7.4.4
+nvidia-curand-cu12==10.3.7.77
+fsspec==2025.10.0
+click==8.1.8
+nvidia-cuda-nvrtc-cu12==12.6.77
+gymnasium==0.29.1
+numpy==1.23.5
+imageio-ffmpeg==0.6.0
+nvidia-nccl-cu12==2.26.2
+urllib3==2.6.3
+packaging==26.0
+zipp==3.23.1
+nvidia-cufile-cu12==1.11.1.6
+nvidia-cudnn-cu12==9.5.1.17
+triton==3.3.1
+filelock==3.19.1
+Farama-Notifications==0.0.4
+grpcio==1.80.0
+iniconfig==2.1.0
+typing_extensions==4.15.0
+PyYAML==6.0.3
+nvidia-cusolver-cu12==11.7.1.2
+nvidia-cuda-runtime-cu12==12.6.77
+pygame==2.6.1
+requests==2.32.5
+pydantic_core==2.46.3
+torch==2.7.1
+nvidia-cufft-cu12==11.3.0.4
+eval_type_backport==0.3.1
+wheel==0.38.4
+typing-inspection==0.4.2
+nvidia-nvjitlink-cu12==12.6.85
diff --git a/wandb/run-20260421_155543-quaracjv/files/runs/CartPole-v1_ppo_1_1776779742/events.out.tfevents.1776779745.BL-LFERNANDEZ.local.eurecat.org.209486.0 b/wandb/run-20260421_155543-quaracjv/files/runs/CartPole-v1_ppo_1_1776779742/events.out.tfevents.1776779745.BL-LFERNANDEZ.local.eurecat.org.209486.0
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,3 @@
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+oid sha256:cf0bd5e4e5ca016f6f0f843bcf53caec5ac7738c358312f90e988b79c6ff5c97
+size 688
diff --git a/wandb/run-20260421_155543-quaracjv/files/wandb-metadata.json b/wandb/run-20260421_155543-quaracjv/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..6af32686941e40d6f529c823ed64b02fe29d5d40
--- /dev/null
+++ b/wandb/run-20260421_155543-quaracjv/files/wandb-metadata.json
@@ -0,0 +1,32 @@
+{
+  "os": "Linux-6.17.0-20-generic-x86_64-with-glibc2.39",
+  "python": "CPython 3.9.5",
+  "startedAt": "2026-04-21T13:55:43.739027Z",
+  "args": [
+    "--track",
+    "--capture-video"
+  ],
+  "program": "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py",
+  "codePath": "ppo.py",
+  "codePathLocal": "ppo.py",
+  "git": {
+    "remote": "https://github.com/loisonchambers/Unit-8-RL-course.git",
+    "commit": "e1dc52c8934ce180fef18fbdf53a72281f134a52"
+  },
+  "email": "loisfernandezcmp@gmail.com",
+  "root": "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8",
+  "host": "BL-LFERNANDEZ.local.eurecat.org",
+  "executable": "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/bin/python",
+  "cpu_count": 12,
+  "cpu_count_logical": 14,
+  "disk": {
+    "/": {
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+      "used": "141826818048"
+    }
+  },
+  "memory": {
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+  },
+  "writerId": "bp1188b62nh6vd31amfonzgm7c33xmvg"
+}
\ No newline at end of file
diff --git a/wandb/run-20260421_155543-quaracjv/files/wandb-summary.json b/wandb/run-20260421_155543-quaracjv/files/wandb-summary.json
new file mode 100644
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--- /dev/null
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diff --git a/wandb/run-20260421_155543-quaracjv/logs/debug-core.log b/wandb/run-20260421_155543-quaracjv/logs/debug-core.log
new file mode 100644
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--- /dev/null
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diff --git a/wandb/run-20260421_155543-quaracjv/logs/debug-internal.log b/wandb/run-20260421_155543-quaracjv/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..1d71095019138f08d307b9c5a28667c04fede9d4
--- /dev/null
+++ b/wandb/run-20260421_155543-quaracjv/logs/debug-internal.log
@@ -0,0 +1,19 @@
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diff --git a/wandb/run-20260421_155543-quaracjv/logs/debug.log b/wandb/run-20260421_155543-quaracjv/logs/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..24fe13b4858db90913eeab9f1c1a4bba98f87eae
--- /dev/null
+++ b/wandb/run-20260421_155543-quaracjv/logs/debug.log
@@ -0,0 +1,22 @@
+2026-04-21 15:55:43,741 INFO    MainThread:209486 [wandb_setup.py:_flush():81] Current SDK version is 0.26.0
+2026-04-21 15:55:43,741 INFO    MainThread:209486 [wandb_setup.py:_flush():81] Configure stats pid to 209486
+2026-04-21 15:55:43,741 INFO    MainThread:209486 [wandb_setup.py:_flush():81] Loading settings from environment variables
+2026-04-21 15:55:43,741 INFO    MainThread:209486 [wandb_init.py:setup_run_log_directory():721] Logging user logs to /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/wandb/run-20260421_155543-quaracjv/logs/debug.log
+2026-04-21 15:55:43,741 INFO    MainThread:209486 [wandb_init.py:setup_run_log_directory():722] Logging internal logs to /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/wandb/run-20260421_155543-quaracjv/logs/debug-internal.log
+2026-04-21 15:55:43,769 INFO    MainThread:209486 [wandb_init.py:init():848] calling init triggers
+2026-04-21 15:55:43,769 INFO    MainThread:209486 [wandb_init.py:init():853] wandb.init called with sweep_config: {}
+config: {'exp_name': 'ppo', 'gym_id': 'CartPole-v1', 'learning_rate': 0.00025, 'seed': 1, 'total_timesteps': 25000, 'torch_deterministic': True, 'cuda': True, 'track': True, 'wandb_project_name': 'cleanRL', 'wandb_entity': None, 'num_envs': 4, 'capture_video': True, 'num_steps': 128, 'anneal_lr': True, 'gae': True, 'gamma': 0.99, 'gae_lambda': 0.95, 'num_minibatches': 4, 'update_epochs': 4, 'norm_adv': True, 'clip_coef': 0.2, 'clip_vloss': True, 'ent_coef': 0.01, 'vf_coef': 0.5, 'max_grad_norm': 0.5, 'target_kl': None, 'batch_size': 512, 'minibatch_size': 128, '_wandb': {'code_path': 'code/ppo.py'}}
+2026-04-21 15:55:43,769 INFO    MainThread:209486 [wandb_init.py:init():896] starting backend
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+2026-04-21 15:55:45,014 INFO    MainThread:209486 [wandb_run.py:_redirect():2460] Wrapping output streams.
+2026-04-21 15:55:45,014 INFO    MainThread:209486 [wandb_run.py:_redirect():2483] Redirects installed.
+2026-04-21 15:55:45,020 INFO    MainThread:209486 [wandb_init.py:init():1098] run started, returning control to user process
+2026-04-21 15:55:45,020 INFO    MainThread:209486 [wandb_run.py:_tensorboard_callback():1632] tensorboard callback: runs/CartPole-v1_ppo_1_1776779742, True
+2026-04-21 15:55:45,036 INFO    wandb-AsyncioManager-main:209486 [service_client.py:_forward_responses():134] Reached EOF.
+2026-04-21 15:55:45,036 INFO    wandb-AsyncioManager-main:209486 [mailbox.py:close():155] Closing mailbox, abandoning 1 handles.
diff --git a/wandb/run-20260421_155543-quaracjv/run-quaracjv.wandb b/wandb/run-20260421_155543-quaracjv/run-quaracjv.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..f853e2393b6bf584142fc4a8982bd63396c24d04
Binary files /dev/null and b/wandb/run-20260421_155543-quaracjv/run-quaracjv.wandb differ
diff --git a/wandb/run-20260421_155809-hog11qe7/files/code/ppo.py b/wandb/run-20260421_155809-hog11qe7/files/code/ppo.py
new file mode 100644
index 0000000000000000000000000000000000000000..29673f194c1e81ddfa6bf33c96f6cfd822f8473a
--- /dev/null
+++ b/wandb/run-20260421_155809-hog11qe7/files/code/ppo.py
@@ -0,0 +1,313 @@
+import argparse
+import os
+from distutils.util import strtobool
+import time
+import random
+import numpy as np
+import gym
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.distributions.categorical import Categorical
+from torch.utils.tensorboard import SummaryWriter
+
+def make_env(gym_id, seed, idx, capture_video, run_name):
+    def thunk():
+        env = gym.make(gym_id)
+        env = gym.wrappers.RecordEpisodeStatistics(env)
+        if capture_video:
+            if idx == 0:
+                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", record_video_trigger=lambda t: t % 1000 == 0)
+        env.seed(seed)
+        env.action_space.seed(seed)
+        env.observation_space.seed(seed)
+        return env
+    return thunk
+
+def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
+    torch.nn.init.orthogonal_(layer.weight, std)
+    torch.nn.init.constant_(layer.bias, bias_const)
+    return layer
+
+class Agent(nn.Module):
+    def __init__(self, envs):
+        super(Agent, self).__init__()
+        self.critic = nn.Sequential(
+            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,1), std=1.),
+        )
+
+        self.actor = nn.Sequential(
+            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
+        )
+
+    def get_value(self, x):
+        return self.critic(x)
+    
+    def get_action_and_value(self, x, action=None):
+        logits = self.actor(x)
+        probs = Categorical(logits=logits)
+        if action is None:
+            action = probs.sample()
+        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+        help='the name of this experiment')
+    parser.add_argument('--gym-id', type=str, default="CartPole-v1",
+        help='the id of the gym environment')
+    parser.add_argument('--learning-rate', type=float, default=2.5e-4,
+        help='the learning rate of the optimizer')
+    parser.add_argument('--seed', type=int, default=1,
+        help='seed of the experiment')
+    parser.add_argument('--total-timesteps', type=int, default=25000,
+        help='total timesteps of the experiments')
+    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+    parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, cuda will not be enabled by default')
+    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
+        help='if toggled, this experiment will be tracked with Weights and Biases')
+    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
+        help="the wandb's project name")
+    parser.add_argument('--wandb-entity', type=str, default=None,
+        help="the entity (team) of wandb's project")
+    parser.add_argument("--num-envs", type=int, default=4,
+    help="the number of parallel game environments")
+    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="whether to capture videos of the agent performances (check out `videos` folder)")
+    parser.add_argument("--num-steps", type=int, default=128,
+        help="the number of steps to run in each environment per policy rollout")
+    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggle learning rate annealing for policy and value networks")  
+    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Use GAE for advantage computation")  
+    parser.add_argument('--gamma', type=float, default=0.99,
+        help='the discount factor gamma')
+    parser.add_argument('--gae-lambda', type=float, default=0.95,
+        help='the lambda for the general advantage estimation')
+    parser.add_argument("--num-minibatches", type=int, default=4,
+        help="the number of mini-batches")
+    parser.add_argument("--update-epochs", type=int, default=4,
+        help="the K epochs to update the policy")
+    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggles advantages normalization")
+    parser.add_argument('--clip-coef', type=float, default=0.2,
+        help='the surrogate clipping coefficient')     
+    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
+    parser.add_argument('--ent-coef', type=float, default=0.01,
+        help='coefficient of the entropy')
+    parser.add_argument('--vf-coef', type=float, default=0.5,
+        help='coefficient of the value function')
+    parser.add_argument('--max-grad-norm', type=float, default=0.5,
+        help='the maximum norm for the gradient clipping')
+    parser.add_argument('--target-kl', type=float, default=None,
+        help='the target KL divergence threshold')
+    args = parser.parse_args()
+    args.batch_size = int(args.num_envs * args.num_steps)
+    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+    return args
+
+if __name__ == "__main__":
+    args = parse_args()
+    print(args)
+    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+    if args.track:
+        import wandb
+
+        wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            sync_tensorboard=True,
+            config=vars(args),
+            name=run_name,
+            monitor_gym=True,
+            save_code=True,
+        )
+    writer =SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
+    )
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
+
+    envs = gym.vector.SyncVectorEnv(
+        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
+    for i in range(args.num_envs)])
+    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
+
+    agent = Agent(envs).to(device)
+    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
+    
+    
+    # ALGO Logic: Storage setup
+    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
+    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
+    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
+
+    #TRY NOT TO MODIFY: start the game
+    global_step = 0
+    start_time =time.time()
+    next_obs = torch.Tensor(envs.reset()).to(device)
+    next_done = torch.zeros(args.num_envs).to(device)
+    num_updates = args.total_timesteps // args.batch_size
+
+    for update in range(1, num_updates+1):
+        # Annealing the rate if instructed to do so.abs
+        if args.anneal_lr:
+            frac = 1.0 -(update -1.0) / num_updates
+            lrnow = frac * args.learning_rate
+            optimizer.param_groups[0]["lr"]=lrnow
+        
+        for step in range(0, args.num_steps):
+            global_step += 1 * args.num_envs
+            obs[step] = next_obs
+            dones[step] = next_done
+
+            # ALGO LOGIC : action logic
+            with torch.no_grad():
+                action, logprob, _, value = agent.get_action_and_value(next_obs)
+                values[step] = value.flatten()
+            actions[step] = action
+            logprobs[step] = logprob
+
+            # TRY NOT TO MODIFY: execute the game and log data.abs
+            next_obs, reward, done, info = envs.step(action.cpu().numpy())
+            rewards[step] = torch.tensor(reward).to(device).view(-1)
+            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
+        
+            if isinstance(info, dict) and "episode" in info:
+                for item in info["episode"]:
+                    if item is not None:
+                        print(f"global_step={global_step}, episodic_return={item['r']}")
+                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
+                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
+                        break
+            
+            #bootstrap reward if not done
+            with torch.no_grad():
+                next_value = agent.get_value(next_obs).reshape(1,-1)
+                if args.gae:
+                    advantages = torch.zeros_like(rewards).to(device)
+                    lastgaelam = 0
+                    for t in reversed(range(args.num_steps)):
+                        if t == args.num_steps - 1:
+                            nextnonterminal = 1.0 - next_done
+                            nextvalues = next_value
+                        else:
+                            nextnonterminal = 1.0 - dones[t+1]
+                            nextvalues = values[t+1]
+                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
+                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
+                    returns =advantages + values
+                else:
+                    returns =torch.zeros_like(rewards).to(device)
+                    for t in reversed(range(args.num_steps)):
+                        if t== args.num_steps - 1:
+                            nextnonterminal = 1.0 -next_done
+                            next_return = next_value
+                        else:
+                            nextnonterminal = 1.0 - dones[t+1]
+                            next_return = returns[t+1]
+                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
+                    advantages = returns - values
+            
+            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
+            b_logprobs =logprobs.reshape(-1)
+            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
+            b_advantages =advantages.reshape(-1)
+            b_returns = returns.reshape(-1)
+            b_values = values.reshape(-1)
+
+            # Optimizaing the policy and value network
+            b_inds = np.arange(args.batch_size)
+            clipfracs = []
+            for epoch in range(args.update_epochs):
+                np.random.shuffle(b_inds)
+                for start in range(0,args.batch_size, args.minibatch_size):
+                    end = start + args.minibatch_size
+                    mb_inds = b_inds[start:end]
+
+                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
+                        b_obs[mb_inds], b_actions.long()[mb_inds]
+                    )
+                    logratio = newlogprob - b_logprobs[mb_inds]
+                    ratio = logratio.exp()
+
+                    with torch.no_grad():
+                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
+                        old_approx_kl = (-logratio).mean()
+                        approx_kl =((ratio-1) - logratio).mean()
+                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
+
+                    mb_advantages = b_advantages[mb_inds]
+                    if args.norm_adv:
+                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
+
+                    # Policy loss
+                    pg_loss1 = -mb_advantages * ratio
+                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
+                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
+
+                    # Value loss
+                    newvalue = newvalue.view(-1)
+                    if args.clip_vloss:
+                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
+                        v_clipped = b_values[mb_inds] + torch.clamp(
+                            newvalue - b_values[mb_inds],
+                            -args.clip_coef,
+                            args.clip_coef
+                        )
+                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
+                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
+                        v_loss = 0.5 * v_loss_max.mean()
+                    else:
+                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
+                    
+                    # Entropy loss
+                    entropy_loss = entropy.mean()
+                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
+
+                    optimizer.zero_grad()
+                    loss.backward()
+                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
+                    optimizer.step()
+            
+                if args.target_kl is not None:
+                    if approx_kl > args.target_kl:
+                        break
+
+        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
+        var_y = np.var(y_true)
+        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
+
+        # TRY NOT TO MODIFY: record rewards for plotting purposes
+        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
+        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
+        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
+        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
+        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
+        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
+        writer.add_scalar("losses/explained_variance",explained_var, global_step)
+        print("SPS:", int(global_step / (time.time() - start_time)))
+        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
+
+    envs.close()
+    writer.close()
\ No newline at end of file
diff --git a/wandb/run-20260421_155809-hog11qe7/files/diff.patch b/wandb/run-20260421_155809-hog11qe7/files/diff.patch
new file mode 100644
index 0000000000000000000000000000000000000000..45dde9260ef7a264c8d256e5e3af42bec023f823
--- /dev/null
+++ b/wandb/run-20260421_155809-hog11qe7/files/diff.patch
@@ -0,0 +1,1317 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..fb998f6 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
++    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"},
++    {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"},
++]
++
++[[package]]
++name = "charset-normalizer"
++version = "3.4.7"
++description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[[package]]
++name = "click"
++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "eval_type_backport-0.3.1-py3-none-any.whl", hash = "sha256:279ab641905e9f11129f56a8a78f493518515b83402b860f6f06dd7c011fdfa8"},
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++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
++optional = false
++python-versions = ">=3.7"
++files = [
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++    {file = "gitdb-4.0.12.tar.gz", hash = "sha256:5ef71f855d191a3326fcfbc0d5da835f26b13fbcba60c32c21091c349ffdb571"},
++]
++
++[package.dependencies]
++smmap = ">=3.0.1,<6"
++
++[[package]]
++name = "gitpython"
++version = "3.1.46"
++description = "GitPython is a Python library used to interact with Git repositories"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "gitpython-3.1.46-py3-none-any.whl", hash = "sha256:79812ed143d9d25b6d176a10bb511de0f9c67b1fa641d82097b0ab90398a2058"},
++    {file = "gitpython-3.1.46.tar.gz", hash = "sha256:400124c7d0ef4ea03f7310ac2fbf7151e09ff97f2a3288d64a440c584a29c37f"},
++]
++
++[package.dependencies]
++gitdb = ">=4.0.1,<5"
++typing-extensions = {version = ">=3.10.0.2", markers = "python_version < \"3.10\""}
++
++[package.extras]
++doc = ["sphinx (>=7.1.2,<7.2)", "sphinx-autodoc-typehints", "sphinx_rtd_theme"]
++test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy (==1.18.2)", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
++
+ [[package]]
+ name = "grpcio"
+ version = "1.80.0"
+@@ -303,6 +524,46 @@ typing-extensions = ">=4.12,<5.0"
+ [package.extras]
+ protobuf = ["grpcio-tools (>=1.80.0)"]
+ 
++[[package]]
++name = "gym"
++version = "0.24.0"
++description = "Gym: A universal API for reinforcement learning environments"
++optional = false
++python-versions = ">=3.6"
++files = [
++    {file = "gym-0.24.0.tar.gz", hash = "sha256:69f96424be40d23088be978b61f45c23911e2ccddafad08cf2fac608e8bd86e4"},
++]
++
++[package.dependencies]
++cloudpickle = ">=1.2.0"
++gym_notices = ">=0.0.4"
++importlib_metadata = {version = ">=4.8.0", markers = "python_version < \"3.10\""}
++numpy = ">=1.18.0"
++
++[package.extras]
++accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"]
++all = ["ale-py (>=0.7.5,<0.8.0)", "box2d-py (==2.3.5)", "box2d-py (==2.3.5)", "box2d-py (==2.3.5)", "box2d-py (==2.3.5)", "imageio (>=2.14.1)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "lz4 (>=3.1.0)", "lz4 (>=3.1.0)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "matplotlib (>=3.0)", "matplotlib (>=3.0)", "matplotlib (>=3.0)", "mujoco (==2.2.0)", "mujoco (==2.2.0)", "mujoco_py (>=2.1,<2.2)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "opencv-python (>=3.0)", "opencv-python (>=3.0)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "scipy (>=1.4.1)", "scipy (>=1.4.1)", "scipy (>=1.4.1)", "scipy (>=1.4.1)"]
++atari = ["ale-py (>=0.7.5,<0.8.0)"]
++box2d = ["box2d-py (==2.3.5)", "pygame (==2.1.0)"]
++classic-control = ["pygame (==2.1.0)"]
++mujoco = ["imageio (>=2.14.1)", "mujoco (==2.2.0)"]
++mujoco-py = ["mujoco_py (>=2.1,<2.2)"]
++noatari = ["box2d-py (==2.3.5)", "box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "matplotlib (>=3.0)", "mujoco (==2.2.0)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "scipy (>=1.4.1)", "scipy (>=1.4.1)"]
++nomujoco = ["box2d-py (==2.3.5)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "pygame (==2.1.0)", "scipy (>=1.4.1)"]
++other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "opencv-python (>=3.0)"]
++toy-text = ["pygame (==2.1.0)", "scipy (>=1.4.1)"]
++
++[[package]]
++name = "gym-notices"
++version = "0.1.0"
++description = "Notices for gym"
++optional = false
++python-versions = "*"
++files = [
++    {file = "gym_notices-0.1.0-py3-none-any.whl", hash = "sha256:a943af4446cb619d04fd1e470b9272b4473e08a06d1c7cc9005755a4a0b8c905"},
++    {file = "gym_notices-0.1.0.tar.gz", hash = "sha256:9f9477ef68a8c15e42625d4fa53631237e3e6ae947f325b5c149c081499adc1b"},
++]
++
+ [[package]]
+ name = "gymnasium"
+ version = "0.29.1"
+@@ -334,6 +595,36 @@ other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-pyt
+ testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
+ toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+ 
++[[package]]
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++description = "Internationalized Domain Names in Applications (IDNA)"
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++
++[[package]]
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+ 
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+ 
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+@@ -919,6 +1193,22 @@ tests = ["check-manifest", "coverage (>=7.4.2)", "defusedxml", "markdown2", "ole
+ typing = ["typing-extensions"]
+ xmp = ["defusedxml"]
+ 
++[[package]]
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++
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++type = ["mypy (>=1.14.1)"]
++
+ [[package]]
+ name = "pluggy"
+ version = "1.6.0"
+@@ -953,6 +1243,228 @@ files = [
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+ 
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++
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++    {file = "sentry_sdk-2.58.0-py2.py3-none-any.whl", hash = "sha256:688d1c704ddecf382ea3326f21a67453d4caa95592d722b7c780a36a9d23109e"},
++    {file = "sentry_sdk-2.58.0.tar.gz", hash = "sha256:c1144d947352d54e5b7daa63596d9f848adf684989c06c4f5a659f0c85a18f6f"},
++]
++
++[package.dependencies]
++certifi = "*"
++urllib3 = ">=1.26.11"
++
++[package.extras]
++aiohttp = ["aiohttp (>=3.5)"]
++anthropic = ["anthropic (>=0.16)"]
++arq = ["arq (>=0.23)"]
++asyncio = ["httpcore[asyncio] (==1.*)"]
++asyncpg = ["asyncpg (>=0.23)"]
++beam = ["apache-beam (>=2.12)"]
++bottle = ["bottle (>=0.12.13)"]
++celery = ["celery (>=3)"]
++celery-redbeat = ["celery-redbeat (>=2)"]
++chalice = ["chalice (>=1.16.0)"]
++clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
++django = ["django (>=1.8)"]
++falcon = ["falcon (>=1.4)"]
++fastapi = ["fastapi (>=0.79.0)"]
++flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
++google-genai = ["google-genai (>=1.29.0)"]
++grpcio = ["grpcio (>=1.21.1)", "protobuf (>=3.8.0)"]
++http2 = ["httpcore[http2] (==1.*)"]
++httpx = ["httpx (>=0.16.0)"]
++huey = ["huey (>=2)"]
++huggingface-hub = ["huggingface_hub (>=0.22)"]
++langchain = ["langchain (>=0.0.210)"]
++langgraph = ["langgraph (>=0.6.6)"]
++launchdarkly = ["launchdarkly-server-sdk (>=9.8.0)"]
++litellm = ["litellm (>=1.77.5,!=1.82.7,!=1.82.8)"]
++litestar = ["litestar (>=2.0.0)"]
++loguru = ["loguru (>=0.5)"]
++mcp = ["mcp (>=1.15.0)"]
++openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
++openfeature = ["openfeature-sdk (>=0.7.1)"]
++opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
++opentelemetry-experimental = ["opentelemetry-distro"]
++opentelemetry-otlp = ["opentelemetry-distro[otlp] (>=0.35b0)"]
++pure-eval = ["asttokens", "executing", "pure_eval"]
++pydantic-ai = ["pydantic-ai (>=1.0.0)"]
++pymongo = ["pymongo (>=3.1)"]
++pyspark = ["pyspark (>=2.4.4)"]
++quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
++rq = ["rq (>=0.6)"]
++sanic = ["sanic (>=0.8)"]
++sqlalchemy = ["sqlalchemy (>=1.2)"]
++starlette = ["starlette (>=0.19.1)"]
++starlite = ["starlite (>=1.48)"]
++statsig = ["statsig (>=0.55.3)"]
++tornado = ["tornado (>=6)"]
++unleash = ["UnleashClient (>=6.0.1)"]
++
++[[package]]
++name = "setuptools"
++version = "65.5.0"
++description = "Easily download, build, install, upgrade, and uninstall Python packages"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "setuptools-65.5.0-py3-none-any.whl", hash = "sha256:f62ea9da9ed6289bfe868cd6845968a2c854d1427f8548d52cae02a42b4f0356"},
++    {file = "setuptools-65.5.0.tar.gz", hash = "sha256:512e5536220e38146176efb833d4a62aa726b7bbff82cfbc8ba9eaa3996e0b17"},
++]
++
++[package.extras]
++docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
++testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
++testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
++
++[[package]]
++name = "smmap"
++version = "5.0.3"
++description = "A pure Python implementation of a sliding window memory map manager"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "smmap-5.0.3-py3-none-any.whl", hash = "sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f"},
++    {file = "smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c"},
++]
+ 
+ [[package]]
+ name = "sympy"
+@@ -1197,6 +1882,81 @@ files = [
+     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+ ]
+ 
++[[package]]
++name = "typing-inspection"
++version = "0.4.2"
++description = "Runtime typing introspection tools"
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
++    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
++]
++
++[package.dependencies]
++typing-extensions = ">=4.12.0"
++
++[[package]]
++name = "urllib3"
++version = "2.6.3"
++description = "HTTP library with thread-safe connection pooling, file post, and more."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
++brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"]
++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:1ece94a2a5eda1d0e3a2d8a2fd28aa0187705d6efa5ac4c0b8680083583b7ec1"},
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:92f6f303fe2af50e3f711833a835150f9b4d8082874bfd9868cf15491ea2947e"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:a17aae051a31831388cff880251c1b5bc38fbf6a283a0ee7c543709e8e9633d1"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:28a14ffc014e523498d077dfde12839b7be586ca8c3190e72e7167c1aea6ee4c"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1996,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "d9bc912ac8bb974295bc3f60161f965078b9162433f639309601f9a5cdeb401b"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..29673f1 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -2,8 +2,62 @@ import argparse
+ import os
+ from distutils.util import strtobool
+ import time
++import random
++import numpy as np
++import gym
++import torch
++import torch.nn as nn
++import torch.optim as optim
++from torch.distributions.categorical import Categorical
+ from torch.utils.tensorboard import SummaryWriter
+ 
++def make_env(gym_id, seed, idx, capture_video, run_name):
++    def thunk():
++        env = gym.make(gym_id)
++        env = gym.wrappers.RecordEpisodeStatistics(env)
++        if capture_video:
++            if idx == 0:
++                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", record_video_trigger=lambda t: t % 1000 == 0)
++        env.seed(seed)
++        env.action_space.seed(seed)
++        env.observation_space.seed(seed)
++        return env
++    return thunk
++
++def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
++    torch.nn.init.orthogonal_(layer.weight, std)
++    torch.nn.init.constant_(layer.bias, bias_const)
++    return layer
++
++class Agent(nn.Module):
++    def __init__(self, envs):
++        super(Agent, self).__init__()
++        self.critic = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,1), std=1.),
++        )
++
++        self.actor = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
++        )
++
++    def get_value(self, x):
++        return self.critic(x)
++    
++    def get_action_and_value(self, x, action=None):
++        logits = self.actor(x)
++        probs = Categorical(logits=logits)
++        if action is None:
++            action = probs.sample()
++        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
++
+ def parse_args():
+     parser = argparse.ArgumentParser()
+     parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+@@ -16,21 +70,244 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
++    parser.add_argument("--num-envs", type=int, default=4,
++    help="the number of parallel game environments")
++    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
++        help="whether to capture videos of the agent performances (check out `videos` folder)")
++    parser.add_argument("--num-steps", type=int, default=128,
++        help="the number of steps to run in each environment per policy rollout")
++    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggle learning rate annealing for policy and value networks")  
++    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Use GAE for advantage computation")  
++    parser.add_argument('--gamma', type=float, default=0.99,
++        help='the discount factor gamma')
++    parser.add_argument('--gae-lambda', type=float, default=0.95,
++        help='the lambda for the general advantage estimation')
++    parser.add_argument("--num-minibatches", type=int, default=4,
++        help="the number of mini-batches")
++    parser.add_argument("--update-epochs", type=int, default=4,
++        help="the K epochs to update the policy")
++    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles advantages normalization")
++    parser.add_argument('--clip-coef', type=float, default=0.2,
++        help='the surrogate clipping coefficient')     
++    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
++    parser.add_argument('--ent-coef', type=float, default=0.01,
++        help='coefficient of the entropy')
++    parser.add_argument('--vf-coef', type=float, default=0.5,
++        help='coefficient of the value function')
++    parser.add_argument('--max-grad-norm', type=float, default=0.5,
++        help='the maximum norm for the gradient clipping')
++    parser.add_argument('--target-kl', type=float, default=None,
++        help='the target KL divergence threshold')
+     args = parser.parse_args()
++    args.batch_size = int(args.num_envs * args.num_steps)
++    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    envs = gym.vector.SyncVectorEnv(
++        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
++    for i in range(args.num_envs)])
++    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
++
++    agent = Agent(envs).to(device)
++    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
++    
++    
++    # ALGO Logic: Storage setup
++    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
++    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
++    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
++
++    #TRY NOT TO MODIFY: start the game
++    global_step = 0
++    start_time =time.time()
++    next_obs = torch.Tensor(envs.reset()).to(device)
++    next_done = torch.zeros(args.num_envs).to(device)
++    num_updates = args.total_timesteps // args.batch_size
++
++    for update in range(1, num_updates+1):
++        # Annealing the rate if instructed to do so.abs
++        if args.anneal_lr:
++            frac = 1.0 -(update -1.0) / num_updates
++            lrnow = frac * args.learning_rate
++            optimizer.param_groups[0]["lr"]=lrnow
++        
++        for step in range(0, args.num_steps):
++            global_step += 1 * args.num_envs
++            obs[step] = next_obs
++            dones[step] = next_done
++
++            # ALGO LOGIC : action logic
++            with torch.no_grad():
++                action, logprob, _, value = agent.get_action_and_value(next_obs)
++                values[step] = value.flatten()
++            actions[step] = action
++            logprobs[step] = logprob
++
++            # TRY NOT TO MODIFY: execute the game and log data.abs
++            next_obs, reward, done, info = envs.step(action.cpu().numpy())
++            rewards[step] = torch.tensor(reward).to(device).view(-1)
++            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
++        
++            if isinstance(info, dict) and "episode" in info:
++                for item in info["episode"]:
++                    if item is not None:
++                        print(f"global_step={global_step}, episodic_return={item['r']}")
++                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
++                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
++                        break
++            
++            #bootstrap reward if not done
++            with torch.no_grad():
++                next_value = agent.get_value(next_obs).reshape(1,-1)
++                if args.gae:
++                    advantages = torch.zeros_like(rewards).to(device)
++                    lastgaelam = 0
++                    for t in reversed(range(args.num_steps)):
++                        if t == args.num_steps - 1:
++                            nextnonterminal = 1.0 - next_done
++                            nextvalues = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            nextvalues = values[t+1]
++                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
++                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
++                    returns =advantages + values
++                else:
++                    returns =torch.zeros_like(rewards).to(device)
++                    for t in reversed(range(args.num_steps)):
++                        if t== args.num_steps - 1:
++                            nextnonterminal = 1.0 -next_done
++                            next_return = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            next_return = returns[t+1]
++                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
++                    advantages = returns - values
++            
++            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
++            b_logprobs =logprobs.reshape(-1)
++            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
++            b_advantages =advantages.reshape(-1)
++            b_returns = returns.reshape(-1)
++            b_values = values.reshape(-1)
++
++            # Optimizaing the policy and value network
++            b_inds = np.arange(args.batch_size)
++            clipfracs = []
++            for epoch in range(args.update_epochs):
++                np.random.shuffle(b_inds)
++                for start in range(0,args.batch_size, args.minibatch_size):
++                    end = start + args.minibatch_size
++                    mb_inds = b_inds[start:end]
++
++                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
++                        b_obs[mb_inds], b_actions.long()[mb_inds]
++                    )
++                    logratio = newlogprob - b_logprobs[mb_inds]
++                    ratio = logratio.exp()
++
++                    with torch.no_grad():
++                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
++                        old_approx_kl = (-logratio).mean()
++                        approx_kl =((ratio-1) - logratio).mean()
++                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
++
++                    mb_advantages = b_advantages[mb_inds]
++                    if args.norm_adv:
++                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
++
++                    # Policy loss
++                    pg_loss1 = -mb_advantages * ratio
++                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
++                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
++
++                    # Value loss
++                    newvalue = newvalue.view(-1)
++                    if args.clip_vloss:
++                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
++                        v_clipped = b_values[mb_inds] + torch.clamp(
++                            newvalue - b_values[mb_inds],
++                            -args.clip_coef,
++                            args.clip_coef
++                        )
++                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
++                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
++                        v_loss = 0.5 * v_loss_max.mean()
++                    else:
++                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
++                    
++                    # Entropy loss
++                    entropy_loss = entropy.mean()
++                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
++
++                    optimizer.zero_grad()
++                    loss.backward()
++                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
++                    optimizer.step()
++            
++                if args.target_kl is not None:
++                    if approx_kl > args.target_kl:
++                        break
++
++        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
++        var_y = np.var(y_true)
++        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
++
++        # TRY NOT TO MODIFY: record rewards for plotting purposes
++        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
++        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
++        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
++        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
++        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
++        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
++        writer.add_scalar("losses/explained_variance",explained_var, global_step)
++        print("SPS:", int(global_step / (time.time() - start_time)))
++        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
++
++    envs.close()
++    writer.close()
+\ No newline at end of file
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..f8ccb9f 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.24"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_155809-hog11qe7/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch b/wandb/run-20260421_155809-hog11qe7/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
new file mode 100644
index 0000000000000000000000000000000000000000..45dde9260ef7a264c8d256e5e3af42bec023f823
--- /dev/null
+++ b/wandb/run-20260421_155809-hog11qe7/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
@@ -0,0 +1,1317 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..fb998f6 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
++    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"},
++    {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"},
++]
++
++[[package]]
++name = "charset-normalizer"
++version = "3.4.7"
++description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "charset_normalizer-3.4.7-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:cdd68a1fb318e290a2077696b7eb7a21a49163c455979c639bf5a5dcdc46617d"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e17b8d5d6a8c47c85e68ca8379def1303fd360c3e22093a807cd34a71cd082b8"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:511ef87c8aec0783e08ac18565a16d435372bc1ac25a91e6ac7f5ef2b0bff790"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:007d05ec7321d12a40227aae9e2bc6dca73f3cb21058999a1df9e193555a9dcc"},
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++
++[[package]]
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++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++smmap = ">=3.0.1,<6"
++
++[[package]]
++name = "gitpython"
++version = "3.1.46"
++description = "GitPython is a Python library used to interact with Git repositories"
++optional = false
++python-versions = ">=3.7"
++files = [
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++    {file = "gitpython-3.1.46.tar.gz", hash = "sha256:400124c7d0ef4ea03f7310ac2fbf7151e09ff97f2a3288d64a440c584a29c37f"},
++]
++
++[package.dependencies]
++gitdb = ">=4.0.1,<5"
++typing-extensions = {version = ">=3.10.0.2", markers = "python_version < \"3.10\""}
++
++[package.extras]
++doc = ["sphinx (>=7.1.2,<7.2)", "sphinx-autodoc-typehints", "sphinx_rtd_theme"]
++test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy (==1.18.2)", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
++
+ [[package]]
+ name = "grpcio"
+ version = "1.80.0"
+@@ -303,6 +524,46 @@ typing-extensions = ">=4.12,<5.0"
+ [package.extras]
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+ 
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+ 
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+ xmp = ["defusedxml"]
+ 
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++    {file = "pyyaml-6.0.3-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3c5677e12444c15717b902a5798264fa7909e41153cdf9ef7ad571b704a63dd9"},
++    {file = "pyyaml-6.0.3-cp39-cp39-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5ed875a24292240029e4483f9d4a4b8a1ae08843b9c54f43fcc11e404532a8a5"},
++    {file = "pyyaml-6.0.3-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0150219816b6a1fa26fb4699fb7daa9caf09eb1999f3b70fb6e786805e80375a"},
++    {file = "pyyaml-6.0.3-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:fa160448684b4e94d80416c0fa4aac48967a969efe22931448d853ada8baf926"},
++    {file = "pyyaml-6.0.3-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:27c0abcb4a5dac13684a37f76e701e054692a9b2d3064b70f5e4eb54810553d7"},
++    {file = "pyyaml-6.0.3-cp39-cp39-win32.whl", hash = "sha256:1ebe39cb5fc479422b83de611d14e2c0d3bb2a18bbcb01f229ab3cfbd8fee7a0"},
++    {file = "pyyaml-6.0.3-cp39-cp39-win_amd64.whl", hash = "sha256:2e71d11abed7344e42a8849600193d15b6def118602c4c176f748e4583246007"},
++    {file = "pyyaml-6.0.3.tar.gz", hash = "sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f"},
++]
++
++[[package]]
++name = "requests"
++version = "2.32.5"
++description = "Python HTTP for Humans."
+ optional = false
+ python-versions = ">=3.9"
+ files = [
+-    {file = "setuptools-82.0.1-py3-none-any.whl", hash = "sha256:a59e362652f08dcd477c78bb6e7bd9d80a7995bc73ce773050228a348ce2e5bb"},
+-    {file = "setuptools-82.0.1.tar.gz", hash = "sha256:7d872682c5d01cfde07da7bccc7b65469d3dca203318515ada1de5eda35efbf9"},
++    {file = "requests-2.32.5-py3-none-any.whl", hash = "sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6"},
++    {file = "requests-2.32.5.tar.gz", hash = "sha256:dbba0bac56e100853db0ea71b82b4dfd5fe2bf6d3754a8893c3af500cec7d7cf"},
+ ]
+ 
++[package.dependencies]
++certifi = ">=2017.4.17"
++charset_normalizer = ">=2,<4"
++idna = ">=2.5,<4"
++urllib3 = ">=1.21.1,<3"
++
+ [package.extras]
+-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.13.0)"]
+-core = ["importlib_metadata (>=6)", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more_itertools", "more_itertools (>=8.8)", "packaging (>=24.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
+-cover = ["pytest-cov"]
+-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+-enabler = ["pytest-enabler (>=2.2)"]
+-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+-type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.18.*)", "pytest-mypy"]
++socks = ["PySocks (>=1.5.6,!=1.5.7)"]
++use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
++
++[[package]]
++name = "sentry-sdk"
++version = "2.58.0"
++description = "Python client for Sentry (https://sentry.io)"
++optional = false
++python-versions = ">=3.6"
++files = [
++    {file = "sentry_sdk-2.58.0-py2.py3-none-any.whl", hash = "sha256:688d1c704ddecf382ea3326f21a67453d4caa95592d722b7c780a36a9d23109e"},
++    {file = "sentry_sdk-2.58.0.tar.gz", hash = "sha256:c1144d947352d54e5b7daa63596d9f848adf684989c06c4f5a659f0c85a18f6f"},
++]
++
++[package.dependencies]
++certifi = "*"
++urllib3 = ">=1.26.11"
++
++[package.extras]
++aiohttp = ["aiohttp (>=3.5)"]
++anthropic = ["anthropic (>=0.16)"]
++arq = ["arq (>=0.23)"]
++asyncio = ["httpcore[asyncio] (==1.*)"]
++asyncpg = ["asyncpg (>=0.23)"]
++beam = ["apache-beam (>=2.12)"]
++bottle = ["bottle (>=0.12.13)"]
++celery = ["celery (>=3)"]
++celery-redbeat = ["celery-redbeat (>=2)"]
++chalice = ["chalice (>=1.16.0)"]
++clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
++django = ["django (>=1.8)"]
++falcon = ["falcon (>=1.4)"]
++fastapi = ["fastapi (>=0.79.0)"]
++flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
++google-genai = ["google-genai (>=1.29.0)"]
++grpcio = ["grpcio (>=1.21.1)", "protobuf (>=3.8.0)"]
++http2 = ["httpcore[http2] (==1.*)"]
++httpx = ["httpx (>=0.16.0)"]
++huey = ["huey (>=2)"]
++huggingface-hub = ["huggingface_hub (>=0.22)"]
++langchain = ["langchain (>=0.0.210)"]
++langgraph = ["langgraph (>=0.6.6)"]
++launchdarkly = ["launchdarkly-server-sdk (>=9.8.0)"]
++litellm = ["litellm (>=1.77.5,!=1.82.7,!=1.82.8)"]
++litestar = ["litestar (>=2.0.0)"]
++loguru = ["loguru (>=0.5)"]
++mcp = ["mcp (>=1.15.0)"]
++openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
++openfeature = ["openfeature-sdk (>=0.7.1)"]
++opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
++opentelemetry-experimental = ["opentelemetry-distro"]
++opentelemetry-otlp = ["opentelemetry-distro[otlp] (>=0.35b0)"]
++pure-eval = ["asttokens", "executing", "pure_eval"]
++pydantic-ai = ["pydantic-ai (>=1.0.0)"]
++pymongo = ["pymongo (>=3.1)"]
++pyspark = ["pyspark (>=2.4.4)"]
++quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
++rq = ["rq (>=0.6)"]
++sanic = ["sanic (>=0.8)"]
++sqlalchemy = ["sqlalchemy (>=1.2)"]
++starlette = ["starlette (>=0.19.1)"]
++starlite = ["starlite (>=1.48)"]
++statsig = ["statsig (>=0.55.3)"]
++tornado = ["tornado (>=6)"]
++unleash = ["UnleashClient (>=6.0.1)"]
++
++[[package]]
++name = "setuptools"
++version = "65.5.0"
++description = "Easily download, build, install, upgrade, and uninstall Python packages"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "setuptools-65.5.0-py3-none-any.whl", hash = "sha256:f62ea9da9ed6289bfe868cd6845968a2c854d1427f8548d52cae02a42b4f0356"},
++    {file = "setuptools-65.5.0.tar.gz", hash = "sha256:512e5536220e38146176efb833d4a62aa726b7bbff82cfbc8ba9eaa3996e0b17"},
++]
++
++[package.extras]
++docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
++testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
++testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
++
++[[package]]
++name = "smmap"
++version = "5.0.3"
++description = "A pure Python implementation of a sliding window memory map manager"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "smmap-5.0.3-py3-none-any.whl", hash = "sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f"},
++    {file = "smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c"},
++]
+ 
+ [[package]]
+ name = "sympy"
+@@ -1197,6 +1882,81 @@ files = [
+     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+ ]
+ 
++[[package]]
++name = "typing-inspection"
++version = "0.4.2"
++description = "Runtime typing introspection tools"
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
++    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
++]
++
++[package.dependencies]
++typing-extensions = ">=4.12.0"
++
++[[package]]
++name = "urllib3"
++version = "2.6.3"
++description = "HTTP library with thread-safe connection pooling, file post, and more."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
++brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"]
++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:1ece94a2a5eda1d0e3a2d8a2fd28aa0187705d6efa5ac4c0b8680083583b7ec1"},
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:92f6f303fe2af50e3f711833a835150f9b4d8082874bfd9868cf15491ea2947e"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:a17aae051a31831388cff880251c1b5bc38fbf6a283a0ee7c543709e8e9633d1"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:28a14ffc014e523498d077dfde12839b7be586ca8c3190e72e7167c1aea6ee4c"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1996,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "d9bc912ac8bb974295bc3f60161f965078b9162433f639309601f9a5cdeb401b"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..29673f1 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -2,8 +2,62 @@ import argparse
+ import os
+ from distutils.util import strtobool
+ import time
++import random
++import numpy as np
++import gym
++import torch
++import torch.nn as nn
++import torch.optim as optim
++from torch.distributions.categorical import Categorical
+ from torch.utils.tensorboard import SummaryWriter
+ 
++def make_env(gym_id, seed, idx, capture_video, run_name):
++    def thunk():
++        env = gym.make(gym_id)
++        env = gym.wrappers.RecordEpisodeStatistics(env)
++        if capture_video:
++            if idx == 0:
++                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", record_video_trigger=lambda t: t % 1000 == 0)
++        env.seed(seed)
++        env.action_space.seed(seed)
++        env.observation_space.seed(seed)
++        return env
++    return thunk
++
++def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
++    torch.nn.init.orthogonal_(layer.weight, std)
++    torch.nn.init.constant_(layer.bias, bias_const)
++    return layer
++
++class Agent(nn.Module):
++    def __init__(self, envs):
++        super(Agent, self).__init__()
++        self.critic = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,1), std=1.),
++        )
++
++        self.actor = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
++        )
++
++    def get_value(self, x):
++        return self.critic(x)
++    
++    def get_action_and_value(self, x, action=None):
++        logits = self.actor(x)
++        probs = Categorical(logits=logits)
++        if action is None:
++            action = probs.sample()
++        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
++
+ def parse_args():
+     parser = argparse.ArgumentParser()
+     parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+@@ -16,21 +70,244 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
++    parser.add_argument("--num-envs", type=int, default=4,
++    help="the number of parallel game environments")
++    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
++        help="whether to capture videos of the agent performances (check out `videos` folder)")
++    parser.add_argument("--num-steps", type=int, default=128,
++        help="the number of steps to run in each environment per policy rollout")
++    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggle learning rate annealing for policy and value networks")  
++    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Use GAE for advantage computation")  
++    parser.add_argument('--gamma', type=float, default=0.99,
++        help='the discount factor gamma')
++    parser.add_argument('--gae-lambda', type=float, default=0.95,
++        help='the lambda for the general advantage estimation')
++    parser.add_argument("--num-minibatches", type=int, default=4,
++        help="the number of mini-batches")
++    parser.add_argument("--update-epochs", type=int, default=4,
++        help="the K epochs to update the policy")
++    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles advantages normalization")
++    parser.add_argument('--clip-coef', type=float, default=0.2,
++        help='the surrogate clipping coefficient')     
++    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
++    parser.add_argument('--ent-coef', type=float, default=0.01,
++        help='coefficient of the entropy')
++    parser.add_argument('--vf-coef', type=float, default=0.5,
++        help='coefficient of the value function')
++    parser.add_argument('--max-grad-norm', type=float, default=0.5,
++        help='the maximum norm for the gradient clipping')
++    parser.add_argument('--target-kl', type=float, default=None,
++        help='the target KL divergence threshold')
+     args = parser.parse_args()
++    args.batch_size = int(args.num_envs * args.num_steps)
++    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    envs = gym.vector.SyncVectorEnv(
++        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
++    for i in range(args.num_envs)])
++    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
++
++    agent = Agent(envs).to(device)
++    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
++    
++    
++    # ALGO Logic: Storage setup
++    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
++    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
++    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
++
++    #TRY NOT TO MODIFY: start the game
++    global_step = 0
++    start_time =time.time()
++    next_obs = torch.Tensor(envs.reset()).to(device)
++    next_done = torch.zeros(args.num_envs).to(device)
++    num_updates = args.total_timesteps // args.batch_size
++
++    for update in range(1, num_updates+1):
++        # Annealing the rate if instructed to do so.abs
++        if args.anneal_lr:
++            frac = 1.0 -(update -1.0) / num_updates
++            lrnow = frac * args.learning_rate
++            optimizer.param_groups[0]["lr"]=lrnow
++        
++        for step in range(0, args.num_steps):
++            global_step += 1 * args.num_envs
++            obs[step] = next_obs
++            dones[step] = next_done
++
++            # ALGO LOGIC : action logic
++            with torch.no_grad():
++                action, logprob, _, value = agent.get_action_and_value(next_obs)
++                values[step] = value.flatten()
++            actions[step] = action
++            logprobs[step] = logprob
++
++            # TRY NOT TO MODIFY: execute the game and log data.abs
++            next_obs, reward, done, info = envs.step(action.cpu().numpy())
++            rewards[step] = torch.tensor(reward).to(device).view(-1)
++            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
++        
++            if isinstance(info, dict) and "episode" in info:
++                for item in info["episode"]:
++                    if item is not None:
++                        print(f"global_step={global_step}, episodic_return={item['r']}")
++                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
++                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
++                        break
++            
++            #bootstrap reward if not done
++            with torch.no_grad():
++                next_value = agent.get_value(next_obs).reshape(1,-1)
++                if args.gae:
++                    advantages = torch.zeros_like(rewards).to(device)
++                    lastgaelam = 0
++                    for t in reversed(range(args.num_steps)):
++                        if t == args.num_steps - 1:
++                            nextnonterminal = 1.0 - next_done
++                            nextvalues = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            nextvalues = values[t+1]
++                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
++                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
++                    returns =advantages + values
++                else:
++                    returns =torch.zeros_like(rewards).to(device)
++                    for t in reversed(range(args.num_steps)):
++                        if t== args.num_steps - 1:
++                            nextnonterminal = 1.0 -next_done
++                            next_return = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            next_return = returns[t+1]
++                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
++                    advantages = returns - values
++            
++            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
++            b_logprobs =logprobs.reshape(-1)
++            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
++            b_advantages =advantages.reshape(-1)
++            b_returns = returns.reshape(-1)
++            b_values = values.reshape(-1)
++
++            # Optimizaing the policy and value network
++            b_inds = np.arange(args.batch_size)
++            clipfracs = []
++            for epoch in range(args.update_epochs):
++                np.random.shuffle(b_inds)
++                for start in range(0,args.batch_size, args.minibatch_size):
++                    end = start + args.minibatch_size
++                    mb_inds = b_inds[start:end]
++
++                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
++                        b_obs[mb_inds], b_actions.long()[mb_inds]
++                    )
++                    logratio = newlogprob - b_logprobs[mb_inds]
++                    ratio = logratio.exp()
++
++                    with torch.no_grad():
++                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
++                        old_approx_kl = (-logratio).mean()
++                        approx_kl =((ratio-1) - logratio).mean()
++                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
++
++                    mb_advantages = b_advantages[mb_inds]
++                    if args.norm_adv:
++                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
++
++                    # Policy loss
++                    pg_loss1 = -mb_advantages * ratio
++                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
++                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
++
++                    # Value loss
++                    newvalue = newvalue.view(-1)
++                    if args.clip_vloss:
++                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
++                        v_clipped = b_values[mb_inds] + torch.clamp(
++                            newvalue - b_values[mb_inds],
++                            -args.clip_coef,
++                            args.clip_coef
++                        )
++                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
++                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
++                        v_loss = 0.5 * v_loss_max.mean()
++                    else:
++                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
++                    
++                    # Entropy loss
++                    entropy_loss = entropy.mean()
++                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
++
++                    optimizer.zero_grad()
++                    loss.backward()
++                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
++                    optimizer.step()
++            
++                if args.target_kl is not None:
++                    if approx_kl > args.target_kl:
++                        break
++
++        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
++        var_y = np.var(y_true)
++        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
++
++        # TRY NOT TO MODIFY: record rewards for plotting purposes
++        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
++        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
++        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
++        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
++        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
++        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
++        writer.add_scalar("losses/explained_variance",explained_var, global_step)
++        print("SPS:", int(global_step / (time.time() - start_time)))
++        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
++
++    envs.close()
++    writer.close()
+\ No newline at end of file
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..f8ccb9f 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.24"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_155809-hog11qe7/files/output.log b/wandb/run-20260421_155809-hog11qe7/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..30ca200dd77412e262e1b218d3bd9e1f5ffbab61
--- /dev/null
+++ b/wandb/run-20260421_155809-hog11qe7/files/output.log
@@ -0,0 +1,26 @@
+Exception ignored in: <function RecordVideo.__del__ at 0x7d68b4740af0>
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/record_video.py", line 166, in __del__
+    self.close_video_recorder()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/record_video.py", line 154, in close_video_recorder
+    if self.recording:
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py", line 262, in __getattr__
+    return getattr(self.env, name)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py", line 262, in __getattr__
+    return getattr(self.env, name)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py", line 262, in __getattr__
+    return getattr(self.env, name)
+  [Previous line repeated 988 more times]
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py", line 260, in __getattr__
+    if name.startswith("_"):
+RecursionError: maximum recursion depth exceeded while calling a Python object
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py", line 148, in <module>
+    envs = gym.vector.SyncVectorEnv(
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/vector/sync_vector_env.py", line 51, in __init__
+    self.envs = [env_fn() for env_fn in env_fns]
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/vector/sync_vector_env.py", line 51, in <listcomp>
+    self.envs = [env_fn() for env_fn in env_fns]
+  File "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py", line 20, in thunk
+    env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", record_video_trigger=lambda t: t % 1000 == 0)
+TypeError: __init__() got an unexpected keyword argument 'record_video_trigger'
diff --git a/wandb/run-20260421_155809-hog11qe7/files/requirements.txt b/wandb/run-20260421_155809-hog11qe7/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..dadfa79626f33964aefccccf43af32c292b97928
--- /dev/null
+++ b/wandb/run-20260421_155809-hog11qe7/files/requirements.txt
@@ -0,0 +1,70 @@
+mpmath==1.3.0
+nvidia-nvtx-cu12==12.6.77
+pluggy==1.6.0
+charset-normalizer==3.4.7
+Markdown==3.9
+protobuf==6.33.6
+Werkzeug==3.1.8
+gym-notices==0.1.0
+tomli==2.4.1
+GitPython==3.1.46
+nvidia-cuda-cupti-cu12==12.6.80
+MarkupSafe==3.0.3
+certifi==2026.2.25
+nvidia-cublas-cu12==12.6.4.1
+nvidia-cusparselt-cu12==0.6.3
+networkx==3.2.1
+wandb==0.26.0
+tensorboard==2.20.0
+setuptools==65.5.0
+nvidia-cusparse-cu12==12.5.4.2
+gitdb==4.0.12
+pip==24.0
+Jinja2==3.1.6
+pydantic==2.13.3
+coverage==7.10.7
+sentry-sdk==2.58.0
+exceptiongroup==1.3.1
+sympy==1.14.0
+annotated-types==0.7.0
+absl-py==2.3.1
+smmap==5.0.3
+importlib_metadata==8.7.1
+idna==3.11
+gym==0.24.0
+pillow==11.3.0
+tensorboard-data-server==0.7.2
+cloudpickle==3.1.2
+platformdirs==4.4.0
+pytest==7.4.4
+nvidia-curand-cu12==10.3.7.77
+fsspec==2025.10.0
+click==8.1.8
+nvidia-cuda-nvrtc-cu12==12.6.77
+gymnasium==0.29.1
+numpy==1.23.5
+imageio-ffmpeg==0.6.0
+nvidia-nccl-cu12==2.26.2
+urllib3==2.6.3
+packaging==26.0
+zipp==3.23.1
+nvidia-cufile-cu12==1.11.1.6
+nvidia-cudnn-cu12==9.5.1.17
+triton==3.3.1
+filelock==3.19.1
+Farama-Notifications==0.0.4
+grpcio==1.80.0
+iniconfig==2.1.0
+typing_extensions==4.15.0
+PyYAML==6.0.3
+nvidia-cusolver-cu12==11.7.1.2
+nvidia-cuda-runtime-cu12==12.6.77
+pygame==2.6.1
+requests==2.32.5
+pydantic_core==2.46.3
+torch==2.7.1
+nvidia-cufft-cu12==11.3.0.4
+eval_type_backport==0.3.1
+wheel==0.38.4
+typing-inspection==0.4.2
+nvidia-nvjitlink-cu12==12.6.85
diff --git a/wandb/run-20260421_155809-hog11qe7/files/wandb-metadata.json b/wandb/run-20260421_155809-hog11qe7/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..74eac8bd3ddf861967d5aaee2bf36351873f2d44
--- /dev/null
+++ b/wandb/run-20260421_155809-hog11qe7/files/wandb-metadata.json
@@ -0,0 +1,32 @@
+{
+  "os": "Linux-6.17.0-20-generic-x86_64-with-glibc2.39",
+  "python": "CPython 3.9.5",
+  "startedAt": "2026-04-21T13:58:09.157924Z",
+  "args": [
+    "--track",
+    "--capture-video"
+  ],
+  "program": "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py",
+  "codePath": "ppo.py",
+  "codePathLocal": "ppo.py",
+  "git": {
+    "remote": "https://github.com/loisonchambers/Unit-8-RL-course.git",
+    "commit": "e1dc52c8934ce180fef18fbdf53a72281f134a52"
+  },
+  "email": "loisfernandezcmp@gmail.com",
+  "root": "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8",
+  "host": "BL-LFERNANDEZ.local.eurecat.org",
+  "executable": "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/bin/python",
+  "cpu_count": 12,
+  "cpu_count_logical": 14,
+  "disk": {
+    "/": {
+      "total": "499680116736",
+      "used": "141830594560"
+    }
+  },
+  "memory": {
+    "total": "33076023296"
+  },
+  "writerId": "d5ik36d21px4q6ms0ya33yr1906xkc1x"
+}
\ No newline at end of file
diff --git a/wandb/run-20260421_155809-hog11qe7/logs/debug-core.log b/wandb/run-20260421_155809-hog11qe7/logs/debug-core.log
new file mode 100644
index 0000000000000000000000000000000000000000..8adfbdc6e769b175d240723cc6868dc7f52fe9b0
--- /dev/null
+++ b/wandb/run-20260421_155809-hog11qe7/logs/debug-core.log
@@ -0,0 +1,12 @@
+{"time":"2026-04-21T15:58:09.234457394+02:00","level":"INFO","msg":"main: starting server","port-filename":"/tmp/tmpvd9mnxm3/port-210132.txt","pid":210132,"detached":false,"idle-timeout":600000000000,"log-level":0,"disable-analytics":false,"shutdown-on-parent-exit":false,"enable-dcgm-profiling":false}
+{"time":"2026-04-21T15:58:09.234872884+02:00","level":"INFO","msg":"server: will exit if parent process dies","ppid":210132}
+{"time":"2026-04-21T15:58:09.234857169+02:00","level":"INFO","msg":"server: accepting connections","addr":{"Name":"/tmp/wandb-210132-210165-4160761388/socket","Net":"unix"}}
+{"time":"2026-04-21T15:58:09.420776844+02:00","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"1(@)"}
+{"time":"2026-04-21T15:58:09.44294753+02:00","level":"INFO","msg":"handleInformInit: received","streamId":"hog11qe7","id":"1(@)"}
+{"time":"2026-04-21T15:58:09.729843983+02:00","level":"INFO","msg":"handleInformInit: stream started","streamId":"hog11qe7","id":"1(@)"}
+{"time":"2026-04-21T15:58:10.251179401+02:00","level":"INFO","msg":"handleInformTeardown: server teardown initiated","id":"1(@)"}
+{"time":"2026-04-21T15:58:10.251224773+02:00","level":"INFO","msg":"server is shutting down"}
+{"time":"2026-04-21T15:58:10.251232037+02:00","level":"INFO","msg":"connection: closing","id":"1(@)"}
+{"time":"2026-04-21T15:58:10.251287156+02:00","level":"INFO","msg":"server: listener closed","addr":{"Name":"/tmp/wandb-210132-210165-4160761388/socket","Net":"unix"}}
+{"time":"2026-04-21T15:58:10.251318027+02:00","level":"INFO","msg":"connection: closed successfully","id":"1(@)"}
+{"time":"2026-04-21T15:58:17.063287342+02:00","level":"INFO","msg":"server: parent process exited, terminating service process"}
diff --git a/wandb/run-20260421_155809-hog11qe7/logs/debug-internal.log b/wandb/run-20260421_155809-hog11qe7/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..f54fcb257a4397781bf8c87f70231af266902605
--- /dev/null
+++ b/wandb/run-20260421_155809-hog11qe7/logs/debug-internal.log
@@ -0,0 +1,10 @@
+{"time":"2026-04-21T15:58:09.443075205+02:00","level":"INFO","msg":"wandb-core"}
+{"time":"2026-04-21T15:58:09.443800271+02:00","level":"INFO","msg":"stream: starting","core version":"0.26.0"}
+{"time":"2026-04-21T15:58:09.72955678+02:00","level":"INFO","msg":"stream: created new stream","id":"hog11qe7"}
+{"time":"2026-04-21T15:58:09.729667666+02:00","level":"INFO","msg":"handler: started"}
+{"time":"2026-04-21T15:58:09.729830735+02:00","level":"INFO","msg":"stream: started"}
+{"time":"2026-04-21T15:58:09.729953161+02:00","level":"INFO","msg":"sender: started"}
+{"time":"2026-04-21T15:58:09.729944169+02:00","level":"INFO","msg":"writer: started","stream_id":"hog11qe7"}
+{"time":"2026-04-21T15:58:10.248221048+02:00","level":"INFO","msg":"filestream: sending request","total_files":1,"console_offset":0,"console_lines":1}
+{"time":"2026-04-21T15:58:10.251215088+02:00","level":"INFO","msg":"stream: closing"}
+{"time":"2026-04-21T15:58:10.589208188+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
diff --git a/wandb/run-20260421_155809-hog11qe7/logs/debug.log b/wandb/run-20260421_155809-hog11qe7/logs/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..cad9a57fbf83897d0480ef7c96897d0a4f043a77
--- /dev/null
+++ b/wandb/run-20260421_155809-hog11qe7/logs/debug.log
@@ -0,0 +1,103 @@
+2026-04-21 15:58:09,160 INFO    MainThread:210132 [wandb_setup.py:_flush():81] Current SDK version is 0.26.0
+2026-04-21 15:58:09,160 INFO    MainThread:210132 [wandb_setup.py:_flush():81] Configure stats pid to 210132
+2026-04-21 15:58:09,160 INFO    MainThread:210132 [wandb_setup.py:_flush():81] Loading settings from environment variables
+2026-04-21 15:58:09,160 INFO    MainThread:210132 [wandb_init.py:setup_run_log_directory():721] Logging user logs to /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/wandb/run-20260421_155809-hog11qe7/logs/debug.log
+2026-04-21 15:58:09,160 INFO    MainThread:210132 [wandb_init.py:setup_run_log_directory():722] Logging internal logs to /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/wandb/run-20260421_155809-hog11qe7/logs/debug-internal.log
+2026-04-21 15:58:09,185 INFO    MainThread:210132 [wandb_init.py:init():848] calling init triggers
+2026-04-21 15:58:09,186 INFO    MainThread:210132 [wandb_init.py:init():853] wandb.init called with sweep_config: {}
+config: {'exp_name': 'ppo', 'gym_id': 'CartPole-v1', 'learning_rate': 0.00025, 'seed': 1, 'total_timesteps': 25000, 'torch_deterministic': True, 'cuda': True, 'track': True, 'wandb_project_name': 'cleanRL', 'wandb_entity': None, 'num_envs': 4, 'capture_video': True, 'num_steps': 128, 'anneal_lr': True, 'gae': True, 'gamma': 0.99, 'gae_lambda': 0.95, 'num_minibatches': 4, 'update_epochs': 4, 'norm_adv': True, 'clip_coef': 0.2, 'clip_vloss': True, 'ent_coef': 0.01, 'vf_coef': 0.5, 'max_grad_norm': 0.5, 'target_kl': None, 'batch_size': 512, 'minibatch_size': 128, '_wandb': {'code_path': 'code/ppo.py'}}
+2026-04-21 15:58:09,186 INFO    MainThread:210132 [wandb_init.py:init():896] starting backend
+2026-04-21 15:58:09,421 INFO    MainThread:210132 [wandb_init.py:init():911] sending inform_init request
+2026-04-21 15:58:09,440 INFO    MainThread:210132 [wandb_init.py:init():919] backend started and connected
+2026-04-21 15:58:09,451 INFO    MainThread:210132 [wandb_init.py:init():989] updated telemetry
+2026-04-21 15:58:09,474 INFO    MainThread:210132 [wandb_init.py:init():1013] communicating run to backend with 90.0 second timeout
+2026-04-21 15:58:10,189 INFO    MainThread:210132 [wandb_init.py:init():1058] starting run threads in backend
+2026-04-21 15:58:10,235 INFO    MainThread:210132 [wandb_run.py:_console_start():2542] atexit reg
+2026-04-21 15:58:10,235 INFO    MainThread:210132 [wandb_run.py:_redirect():2391] redirect: wrap_raw
+2026-04-21 15:58:10,235 INFO    MainThread:210132 [wandb_run.py:_redirect():2460] Wrapping output streams.
+2026-04-21 15:58:10,235 INFO    MainThread:210132 [wandb_run.py:_redirect():2483] Redirects installed.
+2026-04-21 15:58:10,238 INFO    MainThread:210132 [wandb_init.py:init():1098] run started, returning control to user process
+2026-04-21 15:58:10,238 INFO    MainThread:210132 [wandb_run.py:_tensorboard_callback():1632] tensorboard callback: runs/CartPole-v1_ppo_1_1776779888, True
+2026-04-21 15:58:10,251 INFO    wandb-AsyncioManager-main:210132 [service_client.py:_forward_responses():134] Reached EOF.
+2026-04-21 15:58:10,251 INFO    wandb-AsyncioManager-main:210132 [mailbox.py:close():155] Closing mailbox, abandoning 1 handles.
+2026-04-21 15:58:16,642 ERROR   wandb-AsyncioManager-main:210132 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 45, in publish
+    await self._send_server_request(request)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 87, in _send_server_request
+    await self._drain_writer()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 104, in _drain_writer
+    await self._writer.drain()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 387, in drain
+    await self._protocol._drain_helper()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 190, in _drain_helper
+    raise ConnectionResetError('Connection lost')
+ConnectionResetError: Connection lost
+2026-04-21 15:58:16,644 ERROR   wandb-AsyncioManager-main:210132 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 45, in publish
+    await self._send_server_request(request)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 87, in _send_server_request
+    await self._drain_writer()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 104, in _drain_writer
+    await self._writer.drain()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 387, in drain
+    await self._protocol._drain_helper()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 190, in _drain_helper
+    raise ConnectionResetError('Connection lost')
+ConnectionResetError: Connection lost
+2026-04-21 15:58:16,644 ERROR   wandb-AsyncioManager-main:210132 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 45, in publish
+    await self._send_server_request(request)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 78, in _send_server_request
+    raise self._broken_exc.with_traceback(self._broken_tb)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 87, in _send_server_request
+    await self._drain_writer()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 104, in _drain_writer
+    await self._writer.drain()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 387, in drain
+    await self._protocol._drain_helper()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 190, in _drain_helper
+    raise ConnectionResetError('Connection lost')
+ConnectionResetError: Connection lost
+2026-04-21 15:58:16,644 ERROR   wandb-AsyncioManager-main:210132 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 45, in publish
+    await self._send_server_request(request)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 78, in _send_server_request
+    raise self._broken_exc.with_traceback(self._broken_tb)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 87, in _send_server_request
+    await self._drain_writer()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 104, in _drain_writer
+    await self._writer.drain()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 387, in drain
+    await self._protocol._drain_helper()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 190, in _drain_helper
+    raise ConnectionResetError('Connection lost')
+ConnectionResetError: Connection lost
+2026-04-21 15:58:16,645 ERROR   wandb-AsyncioManager-main:210132 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 45, in publish
+    await self._send_server_request(request)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 78, in _send_server_request
+    raise self._broken_exc.with_traceback(self._broken_tb)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 87, in _send_server_request
+    await self._drain_writer()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 104, in _drain_writer
+    await self._writer.drain()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 387, in drain
+    await self._protocol._drain_helper()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 190, in _drain_helper
+    raise ConnectionResetError('Connection lost')
+ConnectionResetError: Connection lost
diff --git a/wandb/run-20260421_155809-hog11qe7/run-hog11qe7.wandb b/wandb/run-20260421_155809-hog11qe7/run-hog11qe7.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..30977402b08eb894463b83d7de7a007255879a32
Binary files /dev/null and b/wandb/run-20260421_155809-hog11qe7/run-hog11qe7.wandb differ
diff --git a/wandb/run-20260421_155831-vt5kivvj/files/code/ppo.py b/wandb/run-20260421_155831-vt5kivvj/files/code/ppo.py
new file mode 100644
index 0000000000000000000000000000000000000000..29673f194c1e81ddfa6bf33c96f6cfd822f8473a
--- /dev/null
+++ b/wandb/run-20260421_155831-vt5kivvj/files/code/ppo.py
@@ -0,0 +1,313 @@
+import argparse
+import os
+from distutils.util import strtobool
+import time
+import random
+import numpy as np
+import gym
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.distributions.categorical import Categorical
+from torch.utils.tensorboard import SummaryWriter
+
+def make_env(gym_id, seed, idx, capture_video, run_name):
+    def thunk():
+        env = gym.make(gym_id)
+        env = gym.wrappers.RecordEpisodeStatistics(env)
+        if capture_video:
+            if idx == 0:
+                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", record_video_trigger=lambda t: t % 1000 == 0)
+        env.seed(seed)
+        env.action_space.seed(seed)
+        env.observation_space.seed(seed)
+        return env
+    return thunk
+
+def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
+    torch.nn.init.orthogonal_(layer.weight, std)
+    torch.nn.init.constant_(layer.bias, bias_const)
+    return layer
+
+class Agent(nn.Module):
+    def __init__(self, envs):
+        super(Agent, self).__init__()
+        self.critic = nn.Sequential(
+            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,1), std=1.),
+        )
+
+        self.actor = nn.Sequential(
+            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
+        )
+
+    def get_value(self, x):
+        return self.critic(x)
+    
+    def get_action_and_value(self, x, action=None):
+        logits = self.actor(x)
+        probs = Categorical(logits=logits)
+        if action is None:
+            action = probs.sample()
+        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+        help='the name of this experiment')
+    parser.add_argument('--gym-id', type=str, default="CartPole-v1",
+        help='the id of the gym environment')
+    parser.add_argument('--learning-rate', type=float, default=2.5e-4,
+        help='the learning rate of the optimizer')
+    parser.add_argument('--seed', type=int, default=1,
+        help='seed of the experiment')
+    parser.add_argument('--total-timesteps', type=int, default=25000,
+        help='total timesteps of the experiments')
+    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+    parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, cuda will not be enabled by default')
+    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
+        help='if toggled, this experiment will be tracked with Weights and Biases')
+    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
+        help="the wandb's project name")
+    parser.add_argument('--wandb-entity', type=str, default=None,
+        help="the entity (team) of wandb's project")
+    parser.add_argument("--num-envs", type=int, default=4,
+    help="the number of parallel game environments")
+    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="whether to capture videos of the agent performances (check out `videos` folder)")
+    parser.add_argument("--num-steps", type=int, default=128,
+        help="the number of steps to run in each environment per policy rollout")
+    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggle learning rate annealing for policy and value networks")  
+    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Use GAE for advantage computation")  
+    parser.add_argument('--gamma', type=float, default=0.99,
+        help='the discount factor gamma')
+    parser.add_argument('--gae-lambda', type=float, default=0.95,
+        help='the lambda for the general advantage estimation')
+    parser.add_argument("--num-minibatches", type=int, default=4,
+        help="the number of mini-batches")
+    parser.add_argument("--update-epochs", type=int, default=4,
+        help="the K epochs to update the policy")
+    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggles advantages normalization")
+    parser.add_argument('--clip-coef', type=float, default=0.2,
+        help='the surrogate clipping coefficient')     
+    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
+    parser.add_argument('--ent-coef', type=float, default=0.01,
+        help='coefficient of the entropy')
+    parser.add_argument('--vf-coef', type=float, default=0.5,
+        help='coefficient of the value function')
+    parser.add_argument('--max-grad-norm', type=float, default=0.5,
+        help='the maximum norm for the gradient clipping')
+    parser.add_argument('--target-kl', type=float, default=None,
+        help='the target KL divergence threshold')
+    args = parser.parse_args()
+    args.batch_size = int(args.num_envs * args.num_steps)
+    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+    return args
+
+if __name__ == "__main__":
+    args = parse_args()
+    print(args)
+    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+    if args.track:
+        import wandb
+
+        wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            sync_tensorboard=True,
+            config=vars(args),
+            name=run_name,
+            monitor_gym=True,
+            save_code=True,
+        )
+    writer =SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
+    )
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
+
+    envs = gym.vector.SyncVectorEnv(
+        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
+    for i in range(args.num_envs)])
+    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
+
+    agent = Agent(envs).to(device)
+    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
+    
+    
+    # ALGO Logic: Storage setup
+    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
+    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
+    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
+
+    #TRY NOT TO MODIFY: start the game
+    global_step = 0
+    start_time =time.time()
+    next_obs = torch.Tensor(envs.reset()).to(device)
+    next_done = torch.zeros(args.num_envs).to(device)
+    num_updates = args.total_timesteps // args.batch_size
+
+    for update in range(1, num_updates+1):
+        # Annealing the rate if instructed to do so.abs
+        if args.anneal_lr:
+            frac = 1.0 -(update -1.0) / num_updates
+            lrnow = frac * args.learning_rate
+            optimizer.param_groups[0]["lr"]=lrnow
+        
+        for step in range(0, args.num_steps):
+            global_step += 1 * args.num_envs
+            obs[step] = next_obs
+            dones[step] = next_done
+
+            # ALGO LOGIC : action logic
+            with torch.no_grad():
+                action, logprob, _, value = agent.get_action_and_value(next_obs)
+                values[step] = value.flatten()
+            actions[step] = action
+            logprobs[step] = logprob
+
+            # TRY NOT TO MODIFY: execute the game and log data.abs
+            next_obs, reward, done, info = envs.step(action.cpu().numpy())
+            rewards[step] = torch.tensor(reward).to(device).view(-1)
+            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
+        
+            if isinstance(info, dict) and "episode" in info:
+                for item in info["episode"]:
+                    if item is not None:
+                        print(f"global_step={global_step}, episodic_return={item['r']}")
+                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
+                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
+                        break
+            
+            #bootstrap reward if not done
+            with torch.no_grad():
+                next_value = agent.get_value(next_obs).reshape(1,-1)
+                if args.gae:
+                    advantages = torch.zeros_like(rewards).to(device)
+                    lastgaelam = 0
+                    for t in reversed(range(args.num_steps)):
+                        if t == args.num_steps - 1:
+                            nextnonterminal = 1.0 - next_done
+                            nextvalues = next_value
+                        else:
+                            nextnonterminal = 1.0 - dones[t+1]
+                            nextvalues = values[t+1]
+                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
+                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
+                    returns =advantages + values
+                else:
+                    returns =torch.zeros_like(rewards).to(device)
+                    for t in reversed(range(args.num_steps)):
+                        if t== args.num_steps - 1:
+                            nextnonterminal = 1.0 -next_done
+                            next_return = next_value
+                        else:
+                            nextnonterminal = 1.0 - dones[t+1]
+                            next_return = returns[t+1]
+                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
+                    advantages = returns - values
+            
+            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
+            b_logprobs =logprobs.reshape(-1)
+            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
+            b_advantages =advantages.reshape(-1)
+            b_returns = returns.reshape(-1)
+            b_values = values.reshape(-1)
+
+            # Optimizaing the policy and value network
+            b_inds = np.arange(args.batch_size)
+            clipfracs = []
+            for epoch in range(args.update_epochs):
+                np.random.shuffle(b_inds)
+                for start in range(0,args.batch_size, args.minibatch_size):
+                    end = start + args.minibatch_size
+                    mb_inds = b_inds[start:end]
+
+                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
+                        b_obs[mb_inds], b_actions.long()[mb_inds]
+                    )
+                    logratio = newlogprob - b_logprobs[mb_inds]
+                    ratio = logratio.exp()
+
+                    with torch.no_grad():
+                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
+                        old_approx_kl = (-logratio).mean()
+                        approx_kl =((ratio-1) - logratio).mean()
+                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
+
+                    mb_advantages = b_advantages[mb_inds]
+                    if args.norm_adv:
+                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
+
+                    # Policy loss
+                    pg_loss1 = -mb_advantages * ratio
+                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
+                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
+
+                    # Value loss
+                    newvalue = newvalue.view(-1)
+                    if args.clip_vloss:
+                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
+                        v_clipped = b_values[mb_inds] + torch.clamp(
+                            newvalue - b_values[mb_inds],
+                            -args.clip_coef,
+                            args.clip_coef
+                        )
+                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
+                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
+                        v_loss = 0.5 * v_loss_max.mean()
+                    else:
+                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
+                    
+                    # Entropy loss
+                    entropy_loss = entropy.mean()
+                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
+
+                    optimizer.zero_grad()
+                    loss.backward()
+                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
+                    optimizer.step()
+            
+                if args.target_kl is not None:
+                    if approx_kl > args.target_kl:
+                        break
+
+        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
+        var_y = np.var(y_true)
+        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
+
+        # TRY NOT TO MODIFY: record rewards for plotting purposes
+        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
+        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
+        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
+        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
+        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
+        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
+        writer.add_scalar("losses/explained_variance",explained_var, global_step)
+        print("SPS:", int(global_step / (time.time() - start_time)))
+        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
+
+    envs.close()
+    writer.close()
\ No newline at end of file
diff --git a/wandb/run-20260421_155831-vt5kivvj/files/diff.patch b/wandb/run-20260421_155831-vt5kivvj/files/diff.patch
new file mode 100644
index 0000000000000000000000000000000000000000..2ebd3cf6ab9fa3be11440edc6e6a88c3adcf8a2a
--- /dev/null
+++ b/wandb/run-20260421_155831-vt5kivvj/files/diff.patch
@@ -0,0 +1,1316 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..60286a8 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
++    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"},
++    {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"},
++]
++
++[[package]]
++name = "charset-normalizer"
++version = "3.4.7"
++description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
++optional = false
++python-versions = ">=3.7"
++files = [
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++
++[[package]]
++name = "click"
++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
++optional = false
++python-versions = ">=3.7"
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+ 
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+ xmp = ["defusedxml"]
+ 
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++    {file = "pyyaml-6.0.3-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3c5677e12444c15717b902a5798264fa7909e41153cdf9ef7ad571b704a63dd9"},
++    {file = "pyyaml-6.0.3-cp39-cp39-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5ed875a24292240029e4483f9d4a4b8a1ae08843b9c54f43fcc11e404532a8a5"},
++    {file = "pyyaml-6.0.3-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0150219816b6a1fa26fb4699fb7daa9caf09eb1999f3b70fb6e786805e80375a"},
++    {file = "pyyaml-6.0.3-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:fa160448684b4e94d80416c0fa4aac48967a969efe22931448d853ada8baf926"},
++    {file = "pyyaml-6.0.3-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:27c0abcb4a5dac13684a37f76e701e054692a9b2d3064b70f5e4eb54810553d7"},
++    {file = "pyyaml-6.0.3-cp39-cp39-win32.whl", hash = "sha256:1ebe39cb5fc479422b83de611d14e2c0d3bb2a18bbcb01f229ab3cfbd8fee7a0"},
++    {file = "pyyaml-6.0.3-cp39-cp39-win_amd64.whl", hash = "sha256:2e71d11abed7344e42a8849600193d15b6def118602c4c176f748e4583246007"},
++    {file = "pyyaml-6.0.3.tar.gz", hash = "sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f"},
++]
++
++[[package]]
++name = "requests"
++version = "2.32.5"
++description = "Python HTTP for Humans."
+ optional = false
+ python-versions = ">=3.9"
+ files = [
+-    {file = "setuptools-82.0.1-py3-none-any.whl", hash = "sha256:a59e362652f08dcd477c78bb6e7bd9d80a7995bc73ce773050228a348ce2e5bb"},
+-    {file = "setuptools-82.0.1.tar.gz", hash = "sha256:7d872682c5d01cfde07da7bccc7b65469d3dca203318515ada1de5eda35efbf9"},
++    {file = "requests-2.32.5-py3-none-any.whl", hash = "sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6"},
++    {file = "requests-2.32.5.tar.gz", hash = "sha256:dbba0bac56e100853db0ea71b82b4dfd5fe2bf6d3754a8893c3af500cec7d7cf"},
+ ]
+ 
++[package.dependencies]
++certifi = ">=2017.4.17"
++charset_normalizer = ">=2,<4"
++idna = ">=2.5,<4"
++urllib3 = ">=1.21.1,<3"
++
+ [package.extras]
+-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.13.0)"]
+-core = ["importlib_metadata (>=6)", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more_itertools", "more_itertools (>=8.8)", "packaging (>=24.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
+-cover = ["pytest-cov"]
+-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+-enabler = ["pytest-enabler (>=2.2)"]
+-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+-type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.18.*)", "pytest-mypy"]
++socks = ["PySocks (>=1.5.6,!=1.5.7)"]
++use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
++
++[[package]]
++name = "sentry-sdk"
++version = "2.58.0"
++description = "Python client for Sentry (https://sentry.io)"
++optional = false
++python-versions = ">=3.6"
++files = [
++    {file = "sentry_sdk-2.58.0-py2.py3-none-any.whl", hash = "sha256:688d1c704ddecf382ea3326f21a67453d4caa95592d722b7c780a36a9d23109e"},
++    {file = "sentry_sdk-2.58.0.tar.gz", hash = "sha256:c1144d947352d54e5b7daa63596d9f848adf684989c06c4f5a659f0c85a18f6f"},
++]
++
++[package.dependencies]
++certifi = "*"
++urllib3 = ">=1.26.11"
++
++[package.extras]
++aiohttp = ["aiohttp (>=3.5)"]
++anthropic = ["anthropic (>=0.16)"]
++arq = ["arq (>=0.23)"]
++asyncio = ["httpcore[asyncio] (==1.*)"]
++asyncpg = ["asyncpg (>=0.23)"]
++beam = ["apache-beam (>=2.12)"]
++bottle = ["bottle (>=0.12.13)"]
++celery = ["celery (>=3)"]
++celery-redbeat = ["celery-redbeat (>=2)"]
++chalice = ["chalice (>=1.16.0)"]
++clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
++django = ["django (>=1.8)"]
++falcon = ["falcon (>=1.4)"]
++fastapi = ["fastapi (>=0.79.0)"]
++flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
++google-genai = ["google-genai (>=1.29.0)"]
++grpcio = ["grpcio (>=1.21.1)", "protobuf (>=3.8.0)"]
++http2 = ["httpcore[http2] (==1.*)"]
++httpx = ["httpx (>=0.16.0)"]
++huey = ["huey (>=2)"]
++huggingface-hub = ["huggingface_hub (>=0.22)"]
++langchain = ["langchain (>=0.0.210)"]
++langgraph = ["langgraph (>=0.6.6)"]
++launchdarkly = ["launchdarkly-server-sdk (>=9.8.0)"]
++litellm = ["litellm (>=1.77.5,!=1.82.7,!=1.82.8)"]
++litestar = ["litestar (>=2.0.0)"]
++loguru = ["loguru (>=0.5)"]
++mcp = ["mcp (>=1.15.0)"]
++openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
++openfeature = ["openfeature-sdk (>=0.7.1)"]
++opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
++opentelemetry-experimental = ["opentelemetry-distro"]
++opentelemetry-otlp = ["opentelemetry-distro[otlp] (>=0.35b0)"]
++pure-eval = ["asttokens", "executing", "pure_eval"]
++pydantic-ai = ["pydantic-ai (>=1.0.0)"]
++pymongo = ["pymongo (>=3.1)"]
++pyspark = ["pyspark (>=2.4.4)"]
++quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
++rq = ["rq (>=0.6)"]
++sanic = ["sanic (>=0.8)"]
++sqlalchemy = ["sqlalchemy (>=1.2)"]
++starlette = ["starlette (>=0.19.1)"]
++starlite = ["starlite (>=1.48)"]
++statsig = ["statsig (>=0.55.3)"]
++tornado = ["tornado (>=6)"]
++unleash = ["UnleashClient (>=6.0.1)"]
++
++[[package]]
++name = "setuptools"
++version = "65.5.0"
++description = "Easily download, build, install, upgrade, and uninstall Python packages"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "setuptools-65.5.0-py3-none-any.whl", hash = "sha256:f62ea9da9ed6289bfe868cd6845968a2c854d1427f8548d52cae02a42b4f0356"},
++    {file = "setuptools-65.5.0.tar.gz", hash = "sha256:512e5536220e38146176efb833d4a62aa726b7bbff82cfbc8ba9eaa3996e0b17"},
++]
++
++[package.extras]
++docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
++testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
++testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
++
++[[package]]
++name = "smmap"
++version = "5.0.3"
++description = "A pure Python implementation of a sliding window memory map manager"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "smmap-5.0.3-py3-none-any.whl", hash = "sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f"},
++    {file = "smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c"},
++]
+ 
+ [[package]]
+ name = "sympy"
+@@ -1197,6 +1881,81 @@ files = [
+     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+ ]
+ 
++[[package]]
++name = "typing-inspection"
++version = "0.4.2"
++description = "Runtime typing introspection tools"
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
++    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
++]
++
++[package.dependencies]
++typing-extensions = ">=4.12.0"
++
++[[package]]
++name = "urllib3"
++version = "2.6.3"
++description = "HTTP library with thread-safe connection pooling, file post, and more."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
++brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"]
++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:1ece94a2a5eda1d0e3a2d8a2fd28aa0187705d6efa5ac4c0b8680083583b7ec1"},
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:92f6f303fe2af50e3f711833a835150f9b4d8082874bfd9868cf15491ea2947e"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:a17aae051a31831388cff880251c1b5bc38fbf6a283a0ee7c543709e8e9633d1"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:28a14ffc014e523498d077dfde12839b7be586ca8c3190e72e7167c1aea6ee4c"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1995,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "de148f2f010b3a360fc5e4a3a96fca4c1f7e0416d9bf2de7a6ee59293e18f270"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..29673f1 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -2,8 +2,62 @@ import argparse
+ import os
+ from distutils.util import strtobool
+ import time
++import random
++import numpy as np
++import gym
++import torch
++import torch.nn as nn
++import torch.optim as optim
++from torch.distributions.categorical import Categorical
+ from torch.utils.tensorboard import SummaryWriter
+ 
++def make_env(gym_id, seed, idx, capture_video, run_name):
++    def thunk():
++        env = gym.make(gym_id)
++        env = gym.wrappers.RecordEpisodeStatistics(env)
++        if capture_video:
++            if idx == 0:
++                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", record_video_trigger=lambda t: t % 1000 == 0)
++        env.seed(seed)
++        env.action_space.seed(seed)
++        env.observation_space.seed(seed)
++        return env
++    return thunk
++
++def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
++    torch.nn.init.orthogonal_(layer.weight, std)
++    torch.nn.init.constant_(layer.bias, bias_const)
++    return layer
++
++class Agent(nn.Module):
++    def __init__(self, envs):
++        super(Agent, self).__init__()
++        self.critic = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,1), std=1.),
++        )
++
++        self.actor = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
++        )
++
++    def get_value(self, x):
++        return self.critic(x)
++    
++    def get_action_and_value(self, x, action=None):
++        logits = self.actor(x)
++        probs = Categorical(logits=logits)
++        if action is None:
++            action = probs.sample()
++        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
++
+ def parse_args():
+     parser = argparse.ArgumentParser()
+     parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+@@ -16,21 +70,244 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
++    parser.add_argument("--num-envs", type=int, default=4,
++    help="the number of parallel game environments")
++    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
++        help="whether to capture videos of the agent performances (check out `videos` folder)")
++    parser.add_argument("--num-steps", type=int, default=128,
++        help="the number of steps to run in each environment per policy rollout")
++    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggle learning rate annealing for policy and value networks")  
++    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Use GAE for advantage computation")  
++    parser.add_argument('--gamma', type=float, default=0.99,
++        help='the discount factor gamma')
++    parser.add_argument('--gae-lambda', type=float, default=0.95,
++        help='the lambda for the general advantage estimation')
++    parser.add_argument("--num-minibatches", type=int, default=4,
++        help="the number of mini-batches")
++    parser.add_argument("--update-epochs", type=int, default=4,
++        help="the K epochs to update the policy")
++    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles advantages normalization")
++    parser.add_argument('--clip-coef', type=float, default=0.2,
++        help='the surrogate clipping coefficient')     
++    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
++    parser.add_argument('--ent-coef', type=float, default=0.01,
++        help='coefficient of the entropy')
++    parser.add_argument('--vf-coef', type=float, default=0.5,
++        help='coefficient of the value function')
++    parser.add_argument('--max-grad-norm', type=float, default=0.5,
++        help='the maximum norm for the gradient clipping')
++    parser.add_argument('--target-kl', type=float, default=None,
++        help='the target KL divergence threshold')
+     args = parser.parse_args()
++    args.batch_size = int(args.num_envs * args.num_steps)
++    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    envs = gym.vector.SyncVectorEnv(
++        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
++    for i in range(args.num_envs)])
++    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
++
++    agent = Agent(envs).to(device)
++    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
++    
++    
++    # ALGO Logic: Storage setup
++    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
++    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
++    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
++
++    #TRY NOT TO MODIFY: start the game
++    global_step = 0
++    start_time =time.time()
++    next_obs = torch.Tensor(envs.reset()).to(device)
++    next_done = torch.zeros(args.num_envs).to(device)
++    num_updates = args.total_timesteps // args.batch_size
++
++    for update in range(1, num_updates+1):
++        # Annealing the rate if instructed to do so.abs
++        if args.anneal_lr:
++            frac = 1.0 -(update -1.0) / num_updates
++            lrnow = frac * args.learning_rate
++            optimizer.param_groups[0]["lr"]=lrnow
++        
++        for step in range(0, args.num_steps):
++            global_step += 1 * args.num_envs
++            obs[step] = next_obs
++            dones[step] = next_done
++
++            # ALGO LOGIC : action logic
++            with torch.no_grad():
++                action, logprob, _, value = agent.get_action_and_value(next_obs)
++                values[step] = value.flatten()
++            actions[step] = action
++            logprobs[step] = logprob
++
++            # TRY NOT TO MODIFY: execute the game and log data.abs
++            next_obs, reward, done, info = envs.step(action.cpu().numpy())
++            rewards[step] = torch.tensor(reward).to(device).view(-1)
++            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
++        
++            if isinstance(info, dict) and "episode" in info:
++                for item in info["episode"]:
++                    if item is not None:
++                        print(f"global_step={global_step}, episodic_return={item['r']}")
++                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
++                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
++                        break
++            
++            #bootstrap reward if not done
++            with torch.no_grad():
++                next_value = agent.get_value(next_obs).reshape(1,-1)
++                if args.gae:
++                    advantages = torch.zeros_like(rewards).to(device)
++                    lastgaelam = 0
++                    for t in reversed(range(args.num_steps)):
++                        if t == args.num_steps - 1:
++                            nextnonterminal = 1.0 - next_done
++                            nextvalues = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            nextvalues = values[t+1]
++                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
++                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
++                    returns =advantages + values
++                else:
++                    returns =torch.zeros_like(rewards).to(device)
++                    for t in reversed(range(args.num_steps)):
++                        if t== args.num_steps - 1:
++                            nextnonterminal = 1.0 -next_done
++                            next_return = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            next_return = returns[t+1]
++                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
++                    advantages = returns - values
++            
++            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
++            b_logprobs =logprobs.reshape(-1)
++            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
++            b_advantages =advantages.reshape(-1)
++            b_returns = returns.reshape(-1)
++            b_values = values.reshape(-1)
++
++            # Optimizaing the policy and value network
++            b_inds = np.arange(args.batch_size)
++            clipfracs = []
++            for epoch in range(args.update_epochs):
++                np.random.shuffle(b_inds)
++                for start in range(0,args.batch_size, args.minibatch_size):
++                    end = start + args.minibatch_size
++                    mb_inds = b_inds[start:end]
++
++                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
++                        b_obs[mb_inds], b_actions.long()[mb_inds]
++                    )
++                    logratio = newlogprob - b_logprobs[mb_inds]
++                    ratio = logratio.exp()
++
++                    with torch.no_grad():
++                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
++                        old_approx_kl = (-logratio).mean()
++                        approx_kl =((ratio-1) - logratio).mean()
++                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
++
++                    mb_advantages = b_advantages[mb_inds]
++                    if args.norm_adv:
++                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
++
++                    # Policy loss
++                    pg_loss1 = -mb_advantages * ratio
++                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
++                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
++
++                    # Value loss
++                    newvalue = newvalue.view(-1)
++                    if args.clip_vloss:
++                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
++                        v_clipped = b_values[mb_inds] + torch.clamp(
++                            newvalue - b_values[mb_inds],
++                            -args.clip_coef,
++                            args.clip_coef
++                        )
++                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
++                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
++                        v_loss = 0.5 * v_loss_max.mean()
++                    else:
++                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
++                    
++                    # Entropy loss
++                    entropy_loss = entropy.mean()
++                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
++
++                    optimizer.zero_grad()
++                    loss.backward()
++                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
++                    optimizer.step()
++            
++                if args.target_kl is not None:
++                    if approx_kl > args.target_kl:
++                        break
++
++        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
++        var_y = np.var(y_true)
++        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
++
++        # TRY NOT TO MODIFY: record rewards for plotting purposes
++        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
++        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
++        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
++        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
++        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
++        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
++        writer.add_scalar("losses/explained_variance",explained_var, global_step)
++        print("SPS:", int(global_step / (time.time() - start_time)))
++        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
++
++    envs.close()
++    writer.close()
+\ No newline at end of file
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..546a851 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.26"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_155831-vt5kivvj/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch b/wandb/run-20260421_155831-vt5kivvj/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
new file mode 100644
index 0000000000000000000000000000000000000000..2ebd3cf6ab9fa3be11440edc6e6a88c3adcf8a2a
--- /dev/null
+++ b/wandb/run-20260421_155831-vt5kivvj/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
@@ -0,0 +1,1316 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..60286a8 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
++    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"},
++    {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"},
++]
++
++[[package]]
++name = "charset-normalizer"
++version = "3.4.7"
++description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "charset_normalizer-3.4.7-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:cdd68a1fb318e290a2077696b7eb7a21a49163c455979c639bf5a5dcdc46617d"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e17b8d5d6a8c47c85e68ca8379def1303fd360c3e22093a807cd34a71cd082b8"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:511ef87c8aec0783e08ac18565a16d435372bc1ac25a91e6ac7f5ef2b0bff790"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:007d05ec7321d12a40227aae9e2bc6dca73f3cb21058999a1df9e193555a9dcc"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:cf29836da5119f3c8a8a70667b0ef5fdca3bb12f80fd06487cfa575b3909b393"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux_2_31_armv7l.whl", hash = "sha256:12d8baf840cc7889b37c7c770f478adea7adce3dcb3944d02ec87508e2dcf153"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:d560742f3c0d62afaccf9f41fe485ed69bd7661a241f86a3ef0f0fb8b1a397af"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:b14b2d9dac08e28bb8046a1a0434b1750eb221c8f5b87a68f4fa11a6f97b5e34"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_armv7l.whl", hash = "sha256:bc17a677b21b3502a21f66a8cc64f5bfad4df8a0b8434d661666f8ce90ac3af1"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:750e02e074872a3fad7f233b47734166440af3cdea0add3e95163110816d6752"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_riscv64.whl", hash = "sha256:4e5163c14bffd570ef2affbfdd77bba66383890797df43dc8b4cc7d6f500bf53"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:6ed74185b2db44f41ef35fd1617c5888e59792da9bbc9190d6c7300617182616"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:94e1885b270625a9a828c9793b4d52a64445299baa1fea5a173bf1d3dd9a1a5a"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-win32.whl", hash = "sha256:6785f414ae0f3c733c437e0f3929197934f526d19dfaa75e18fdb4f94c6fb374"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-win_amd64.whl", hash = "sha256:6696b7688f54f5af4462118f0bfa7c1621eeb87154f77fa04b9295ce7a8f2943"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-win_arm64.whl", hash = "sha256:66671f93accb62ed07da56613636f3641f1a12c13046ce91ffc923721f23c008"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:7641bb8895e77f921102f72833904dcd9901df5d6d72a2ab8f31d04b7e51e4e7"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:202389074300232baeb53ae2569a60901f7efadd4245cf3a3bf0617d60b439d7"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:30b8d1d8c52a48c2c5690e152c169b673487a2a58de1ec7393196753063fcd5e"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:532bc9bf33a68613fd7d65e4b1c71a6a38d7d42604ecf239c77392e9b4e8998c"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2fe249cb4651fd12605b7288b24751d8bfd46d35f12a20b1ba33dea122e690df"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-manylinux_2_31_armv7l.whl", hash = "sha256:65bcd23054beab4d166035cabbc868a09c1a49d1efe458fe8e4361215df40265"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:08e721811161356f97b4059a9ba7bafb23ea5ee2255402c42881c214e173c6b4"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:e060d01aec0a910bdccb8be71faf34e7799ce36950f8294c8bf612cba65a2c9e"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-musllinux_1_2_armv7l.whl", hash = "sha256:38c0109396c4cfc574d502df99742a45c72c08eff0a36158b6f04000043dbf38"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:1c2a768fdd44ee4a9339a9b0b130049139b8ce3c01d2ce09f67f5a68048d477c"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:1a87ca9d5df6fe460483d9a5bbf2b18f620cbed41b432e2bddb686228282d10b"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:d635aab80466bc95771bb78d5370e74d36d1fe31467b6b29b8b57b2a3cd7d22c"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:ae196f021b5e7c78e918242d217db021ed2a6ace2bc6ae94c0fc596221c7f58d"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-win32.whl", hash = "sha256:adb2597b428735679446b46c8badf467b4ca5f5056aae4d51a19f9570301b1ad"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-win_amd64.whl", hash = "sha256:8e385e4267ab76874ae30db04c627faaaf0b509e1ccc11a95b3fc3e83f855c00"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-win_arm64.whl", hash = "sha256:d4a48e5b3c2a489fae013b7589308a40146ee081f6f509e047e0e096084ceca1"},
++    {file = "charset_normalizer-3.4.7-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:eca9705049ad3c7345d574e3510665cb2cf844c2f2dcfe675332677f081cbd46"},
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++]
++
++[[package]]
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++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
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++    {file = "click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a"},
++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "eval_type_backport-0.3.1-py3-none-any.whl", hash = "sha256:279ab641905e9f11129f56a8a78f493518515b83402b860f6f06dd7c011fdfa8"},
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++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
++optional = false
++python-versions = ">=3.7"
++files = [
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++
++[package.dependencies]
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++
++[[package]]
++name = "gitpython"
++version = "3.1.46"
++description = "GitPython is a Python library used to interact with Git repositories"
++optional = false
++python-versions = ">=3.7"
++files = [
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++
++[package.dependencies]
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++typing-extensions = {version = ">=3.10.0.2", markers = "python_version < \"3.10\""}
++
++[package.extras]
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++test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy (==1.18.2)", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
++
+ [[package]]
+ name = "grpcio"
+ version = "1.80.0"
+@@ -303,6 +524,45 @@ typing-extensions = ">=4.12,<5.0"
+ [package.extras]
+ protobuf = ["grpcio-tools (>=1.80.0)"]
+ 
++[[package]]
++name = "gym"
++version = "0.26.0"
++description = "Gym: A universal API for reinforcement learning environments"
++optional = false
++python-versions = ">=3.6"
++files = [
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++
++[package.dependencies]
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++importlib_metadata = {version = ">=4.8.0", markers = "python_version < \"3.10\""}
++numpy = ">=1.18.0"
++
++[package.extras]
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++toy-text = ["pygame (==2.1.0)"]
++
++[[package]]
++name = "gym-notices"
++version = "0.1.0"
++description = "Notices for gym"
++optional = false
++python-versions = "*"
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++]
++
+ [[package]]
+ name = "gymnasium"
+ version = "0.29.1"
+@@ -334,6 +594,36 @@ other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-pyt
+ testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
+ toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+ 
++[[package]]
++name = "idna"
++version = "3.11"
++description = "Internationalized Domain Names in Applications (IDNA)"
++optional = false
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++[[package]]
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+ [[package]]
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+ 
+ [[package]]
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++version = "1.23.5"
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++
++[[package]]
++name = "requests"
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+ python-versions = ">=3.9"
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++socks = ["PySocks (>=1.5.6,!=1.5.7)"]
++use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
++
++[[package]]
++name = "sentry-sdk"
++version = "2.58.0"
++description = "Python client for Sentry (https://sentry.io)"
++optional = false
++python-versions = ">=3.6"
++files = [
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++
++[package.dependencies]
++certifi = "*"
++urllib3 = ">=1.26.11"
++
++[package.extras]
++aiohttp = ["aiohttp (>=3.5)"]
++anthropic = ["anthropic (>=0.16)"]
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++asyncpg = ["asyncpg (>=0.23)"]
++beam = ["apache-beam (>=2.12)"]
++bottle = ["bottle (>=0.12.13)"]
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++chalice = ["chalice (>=1.16.0)"]
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++django = ["django (>=1.8)"]
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++fastapi = ["fastapi (>=0.79.0)"]
++flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
++google-genai = ["google-genai (>=1.29.0)"]
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++http2 = ["httpcore[http2] (==1.*)"]
++httpx = ["httpx (>=0.16.0)"]
++huey = ["huey (>=2)"]
++huggingface-hub = ["huggingface_hub (>=0.22)"]
++langchain = ["langchain (>=0.0.210)"]
++langgraph = ["langgraph (>=0.6.6)"]
++launchdarkly = ["launchdarkly-server-sdk (>=9.8.0)"]
++litellm = ["litellm (>=1.77.5,!=1.82.7,!=1.82.8)"]
++litestar = ["litestar (>=2.0.0)"]
++loguru = ["loguru (>=0.5)"]
++mcp = ["mcp (>=1.15.0)"]
++openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
++openfeature = ["openfeature-sdk (>=0.7.1)"]
++opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
++opentelemetry-experimental = ["opentelemetry-distro"]
++opentelemetry-otlp = ["opentelemetry-distro[otlp] (>=0.35b0)"]
++pure-eval = ["asttokens", "executing", "pure_eval"]
++pydantic-ai = ["pydantic-ai (>=1.0.0)"]
++pymongo = ["pymongo (>=3.1)"]
++pyspark = ["pyspark (>=2.4.4)"]
++quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
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++sqlalchemy = ["sqlalchemy (>=1.2)"]
++starlette = ["starlette (>=0.19.1)"]
++starlite = ["starlite (>=1.48)"]
++statsig = ["statsig (>=0.55.3)"]
++tornado = ["tornado (>=6)"]
++unleash = ["UnleashClient (>=6.0.1)"]
++
++[[package]]
++name = "setuptools"
++version = "65.5.0"
++description = "Easily download, build, install, upgrade, and uninstall Python packages"
++optional = false
++python-versions = ">=3.7"
++files = [
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++    {file = "setuptools-65.5.0.tar.gz", hash = "sha256:512e5536220e38146176efb833d4a62aa726b7bbff82cfbc8ba9eaa3996e0b17"},
++]
++
++[package.extras]
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++testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
++testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
++
++[[package]]
++name = "smmap"
++version = "5.0.3"
++description = "A pure Python implementation of a sliding window memory map manager"
++optional = false
++python-versions = ">=3.7"
++files = [
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++    {file = "smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c"},
++]
+ 
+ [[package]]
+ name = "sympy"
+@@ -1197,6 +1881,81 @@ files = [
+     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+ ]
+ 
++[[package]]
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++description = "Runtime typing introspection tools"
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
++    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
++]
++
++[package.dependencies]
++typing-extensions = ">=4.12.0"
++
++[[package]]
++name = "urllib3"
++version = "2.6.3"
++description = "HTTP library with thread-safe connection pooling, file post, and more."
++optional = false
++python-versions = ">=3.9"
++files = [
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++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
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++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
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++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1995,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "de148f2f010b3a360fc5e4a3a96fca4c1f7e0416d9bf2de7a6ee59293e18f270"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..29673f1 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -2,8 +2,62 @@ import argparse
+ import os
+ from distutils.util import strtobool
+ import time
++import random
++import numpy as np
++import gym
++import torch
++import torch.nn as nn
++import torch.optim as optim
++from torch.distributions.categorical import Categorical
+ from torch.utils.tensorboard import SummaryWriter
+ 
++def make_env(gym_id, seed, idx, capture_video, run_name):
++    def thunk():
++        env = gym.make(gym_id)
++        env = gym.wrappers.RecordEpisodeStatistics(env)
++        if capture_video:
++            if idx == 0:
++                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", record_video_trigger=lambda t: t % 1000 == 0)
++        env.seed(seed)
++        env.action_space.seed(seed)
++        env.observation_space.seed(seed)
++        return env
++    return thunk
++
++def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
++    torch.nn.init.orthogonal_(layer.weight, std)
++    torch.nn.init.constant_(layer.bias, bias_const)
++    return layer
++
++class Agent(nn.Module):
++    def __init__(self, envs):
++        super(Agent, self).__init__()
++        self.critic = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,1), std=1.),
++        )
++
++        self.actor = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
++        )
++
++    def get_value(self, x):
++        return self.critic(x)
++    
++    def get_action_and_value(self, x, action=None):
++        logits = self.actor(x)
++        probs = Categorical(logits=logits)
++        if action is None:
++            action = probs.sample()
++        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
++
+ def parse_args():
+     parser = argparse.ArgumentParser()
+     parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+@@ -16,21 +70,244 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
++    parser.add_argument("--num-envs", type=int, default=4,
++    help="the number of parallel game environments")
++    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
++        help="whether to capture videos of the agent performances (check out `videos` folder)")
++    parser.add_argument("--num-steps", type=int, default=128,
++        help="the number of steps to run in each environment per policy rollout")
++    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggle learning rate annealing for policy and value networks")  
++    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Use GAE for advantage computation")  
++    parser.add_argument('--gamma', type=float, default=0.99,
++        help='the discount factor gamma')
++    parser.add_argument('--gae-lambda', type=float, default=0.95,
++        help='the lambda for the general advantage estimation')
++    parser.add_argument("--num-minibatches", type=int, default=4,
++        help="the number of mini-batches")
++    parser.add_argument("--update-epochs", type=int, default=4,
++        help="the K epochs to update the policy")
++    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles advantages normalization")
++    parser.add_argument('--clip-coef', type=float, default=0.2,
++        help='the surrogate clipping coefficient')     
++    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
++    parser.add_argument('--ent-coef', type=float, default=0.01,
++        help='coefficient of the entropy')
++    parser.add_argument('--vf-coef', type=float, default=0.5,
++        help='coefficient of the value function')
++    parser.add_argument('--max-grad-norm', type=float, default=0.5,
++        help='the maximum norm for the gradient clipping')
++    parser.add_argument('--target-kl', type=float, default=None,
++        help='the target KL divergence threshold')
+     args = parser.parse_args()
++    args.batch_size = int(args.num_envs * args.num_steps)
++    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    envs = gym.vector.SyncVectorEnv(
++        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
++    for i in range(args.num_envs)])
++    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
++
++    agent = Agent(envs).to(device)
++    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
++    
++    
++    # ALGO Logic: Storage setup
++    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
++    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
++    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
++
++    #TRY NOT TO MODIFY: start the game
++    global_step = 0
++    start_time =time.time()
++    next_obs = torch.Tensor(envs.reset()).to(device)
++    next_done = torch.zeros(args.num_envs).to(device)
++    num_updates = args.total_timesteps // args.batch_size
++
++    for update in range(1, num_updates+1):
++        # Annealing the rate if instructed to do so.abs
++        if args.anneal_lr:
++            frac = 1.0 -(update -1.0) / num_updates
++            lrnow = frac * args.learning_rate
++            optimizer.param_groups[0]["lr"]=lrnow
++        
++        for step in range(0, args.num_steps):
++            global_step += 1 * args.num_envs
++            obs[step] = next_obs
++            dones[step] = next_done
++
++            # ALGO LOGIC : action logic
++            with torch.no_grad():
++                action, logprob, _, value = agent.get_action_and_value(next_obs)
++                values[step] = value.flatten()
++            actions[step] = action
++            logprobs[step] = logprob
++
++            # TRY NOT TO MODIFY: execute the game and log data.abs
++            next_obs, reward, done, info = envs.step(action.cpu().numpy())
++            rewards[step] = torch.tensor(reward).to(device).view(-1)
++            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
++        
++            if isinstance(info, dict) and "episode" in info:
++                for item in info["episode"]:
++                    if item is not None:
++                        print(f"global_step={global_step}, episodic_return={item['r']}")
++                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
++                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
++                        break
++            
++            #bootstrap reward if not done
++            with torch.no_grad():
++                next_value = agent.get_value(next_obs).reshape(1,-1)
++                if args.gae:
++                    advantages = torch.zeros_like(rewards).to(device)
++                    lastgaelam = 0
++                    for t in reversed(range(args.num_steps)):
++                        if t == args.num_steps - 1:
++                            nextnonterminal = 1.0 - next_done
++                            nextvalues = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            nextvalues = values[t+1]
++                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
++                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
++                    returns =advantages + values
++                else:
++                    returns =torch.zeros_like(rewards).to(device)
++                    for t in reversed(range(args.num_steps)):
++                        if t== args.num_steps - 1:
++                            nextnonterminal = 1.0 -next_done
++                            next_return = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            next_return = returns[t+1]
++                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
++                    advantages = returns - values
++            
++            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
++            b_logprobs =logprobs.reshape(-1)
++            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
++            b_advantages =advantages.reshape(-1)
++            b_returns = returns.reshape(-1)
++            b_values = values.reshape(-1)
++
++            # Optimizaing the policy and value network
++            b_inds = np.arange(args.batch_size)
++            clipfracs = []
++            for epoch in range(args.update_epochs):
++                np.random.shuffle(b_inds)
++                for start in range(0,args.batch_size, args.minibatch_size):
++                    end = start + args.minibatch_size
++                    mb_inds = b_inds[start:end]
++
++                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
++                        b_obs[mb_inds], b_actions.long()[mb_inds]
++                    )
++                    logratio = newlogprob - b_logprobs[mb_inds]
++                    ratio = logratio.exp()
++
++                    with torch.no_grad():
++                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
++                        old_approx_kl = (-logratio).mean()
++                        approx_kl =((ratio-1) - logratio).mean()
++                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
++
++                    mb_advantages = b_advantages[mb_inds]
++                    if args.norm_adv:
++                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
++
++                    # Policy loss
++                    pg_loss1 = -mb_advantages * ratio
++                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
++                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
++
++                    # Value loss
++                    newvalue = newvalue.view(-1)
++                    if args.clip_vloss:
++                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
++                        v_clipped = b_values[mb_inds] + torch.clamp(
++                            newvalue - b_values[mb_inds],
++                            -args.clip_coef,
++                            args.clip_coef
++                        )
++                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
++                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
++                        v_loss = 0.5 * v_loss_max.mean()
++                    else:
++                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
++                    
++                    # Entropy loss
++                    entropy_loss = entropy.mean()
++                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
++
++                    optimizer.zero_grad()
++                    loss.backward()
++                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
++                    optimizer.step()
++            
++                if args.target_kl is not None:
++                    if approx_kl > args.target_kl:
++                        break
++
++        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
++        var_y = np.var(y_true)
++        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
++
++        # TRY NOT TO MODIFY: record rewards for plotting purposes
++        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
++        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
++        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
++        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
++        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
++        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
++        writer.add_scalar("losses/explained_variance",explained_var, global_step)
++        print("SPS:", int(global_step / (time.time() - start_time)))
++        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
++
++    envs.close()
++    writer.close()
+\ No newline at end of file
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..546a851 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.26"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_155831-vt5kivvj/files/output.log b/wandb/run-20260421_155831-vt5kivvj/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..f826a315358381c4a1fce49da6c5cf4f273d0a38
--- /dev/null
+++ b/wandb/run-20260421_155831-vt5kivvj/files/output.log
@@ -0,0 +1 @@
+Exception ignored in: <function RecordVideo.__del__ at 0x73a3d3dd8ca0>
diff --git a/wandb/run-20260421_155831-vt5kivvj/files/requirements.txt b/wandb/run-20260421_155831-vt5kivvj/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9207b9e211f3ef386f06495297e314b166ab4b21
--- /dev/null
+++ b/wandb/run-20260421_155831-vt5kivvj/files/requirements.txt
@@ -0,0 +1,70 @@
+mpmath==1.3.0
+nvidia-nvtx-cu12==12.6.77
+pluggy==1.6.0
+charset-normalizer==3.4.7
+Markdown==3.9
+protobuf==6.33.6
+Werkzeug==3.1.8
+gym-notices==0.1.0
+tomli==2.4.1
+GitPython==3.1.46
+nvidia-cuda-cupti-cu12==12.6.80
+MarkupSafe==3.0.3
+certifi==2026.2.25
+nvidia-cublas-cu12==12.6.4.1
+nvidia-cusparselt-cu12==0.6.3
+networkx==3.2.1
+wandb==0.26.0
+tensorboard==2.20.0
+setuptools==65.5.0
+nvidia-cusparse-cu12==12.5.4.2
+gitdb==4.0.12
+pip==24.0
+Jinja2==3.1.6
+pydantic==2.13.3
+coverage==7.10.7
+sentry-sdk==2.58.0
+exceptiongroup==1.3.1
+sympy==1.14.0
+annotated-types==0.7.0
+absl-py==2.3.1
+smmap==5.0.3
+importlib_metadata==8.7.1
+idna==3.11
+pillow==11.3.0
+tensorboard-data-server==0.7.2
+cloudpickle==3.1.2
+platformdirs==4.4.0
+pytest==7.4.4
+nvidia-curand-cu12==10.3.7.77
+fsspec==2025.10.0
+click==8.1.8
+nvidia-cuda-nvrtc-cu12==12.6.77
+gymnasium==0.29.1
+numpy==1.23.5
+imageio-ffmpeg==0.6.0
+nvidia-nccl-cu12==2.26.2
+urllib3==2.6.3
+packaging==26.0
+zipp==3.23.1
+nvidia-cufile-cu12==1.11.1.6
+nvidia-cudnn-cu12==9.5.1.17
+triton==3.3.1
+filelock==3.19.1
+Farama-Notifications==0.0.4
+grpcio==1.80.0
+iniconfig==2.1.0
+gym==0.26.0
+typing_extensions==4.15.0
+PyYAML==6.0.3
+nvidia-cusolver-cu12==11.7.1.2
+nvidia-cuda-runtime-cu12==12.6.77
+pygame==2.6.1
+requests==2.32.5
+pydantic_core==2.46.3
+torch==2.7.1
+nvidia-cufft-cu12==11.3.0.4
+eval_type_backport==0.3.1
+wheel==0.38.4
+typing-inspection==0.4.2
+nvidia-nvjitlink-cu12==12.6.85
diff --git a/wandb/run-20260421_155831-vt5kivvj/files/wandb-metadata.json b/wandb/run-20260421_155831-vt5kivvj/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..1e9aae81d6635eb59131f1e8ea9f20b859cb7aa7
--- /dev/null
+++ b/wandb/run-20260421_155831-vt5kivvj/files/wandb-metadata.json
@@ -0,0 +1,32 @@
+{
+  "os": "Linux-6.17.0-20-generic-x86_64-with-glibc2.39",
+  "python": "CPython 3.9.5",
+  "startedAt": "2026-04-21T13:58:31.995794Z",
+  "args": [
+    "--track",
+    "--capture-video"
+  ],
+  "program": "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py",
+  "codePath": "ppo.py",
+  "codePathLocal": "ppo.py",
+  "git": {
+    "remote": "https://github.com/loisonchambers/Unit-8-RL-course.git",
+    "commit": "e1dc52c8934ce180fef18fbdf53a72281f134a52"
+  },
+  "email": "loisfernandezcmp@gmail.com",
+  "root": "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8",
+  "host": "BL-LFERNANDEZ.local.eurecat.org",
+  "executable": "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/bin/python",
+  "cpu_count": 12,
+  "cpu_count_logical": 14,
+  "disk": {
+    "/": {
+      "total": "499680116736",
+      "used": "141834186752"
+    }
+  },
+  "memory": {
+    "total": "33076023296"
+  },
+  "writerId": "f9a7yg18zi5arkmr8s254lwym10atrn1"
+}
\ No newline at end of file
diff --git a/wandb/run-20260421_155831-vt5kivvj/logs/debug-core.log b/wandb/run-20260421_155831-vt5kivvj/logs/debug-core.log
new file mode 100644
index 0000000000000000000000000000000000000000..ea3b4e64fc59ad677a084b746ba45e037151a8cc
--- /dev/null
+++ b/wandb/run-20260421_155831-vt5kivvj/logs/debug-core.log
@@ -0,0 +1,12 @@
+{"time":"2026-04-21T15:58:32.055634245+02:00","level":"INFO","msg":"main: starting server","port-filename":"/tmp/tmphah1e2jr/port-210492.txt","pid":210492,"detached":false,"idle-timeout":600000000000,"log-level":0,"disable-analytics":false,"shutdown-on-parent-exit":false,"enable-dcgm-profiling":false}
+{"time":"2026-04-21T15:58:32.055888174+02:00","level":"INFO","msg":"server: will exit if parent process dies","ppid":210492}
+{"time":"2026-04-21T15:58:32.055883136+02:00","level":"INFO","msg":"server: accepting connections","addr":{"Name":"/tmp/wandb-210492-210526-2166675770/socket","Net":"unix"}}
+{"time":"2026-04-21T15:58:32.251870316+02:00","level":"INFO","msg":"connection: ManageConnectionData: new connection created","id":"1(@)"}
+{"time":"2026-04-21T15:58:32.28082492+02:00","level":"INFO","msg":"handleInformInit: received","streamId":"vt5kivvj","id":"1(@)"}
+{"time":"2026-04-21T15:58:32.56378486+02:00","level":"INFO","msg":"handleInformInit: stream started","streamId":"vt5kivvj","id":"1(@)"}
+{"time":"2026-04-21T15:58:32.986211991+02:00","level":"INFO","msg":"handleInformTeardown: server teardown initiated","id":"1(@)"}
+{"time":"2026-04-21T15:58:32.986269616+02:00","level":"INFO","msg":"server is shutting down"}
+{"time":"2026-04-21T15:58:32.986273372+02:00","level":"INFO","msg":"connection: closing","id":"1(@)"}
+{"time":"2026-04-21T15:58:32.986474291+02:00","level":"INFO","msg":"connection: closed successfully","id":"1(@)"}
+{"time":"2026-04-21T15:58:32.986468825+02:00","level":"INFO","msg":"server: listener closed","addr":{"Name":"/tmp/wandb-210492-210526-2166675770/socket","Net":"unix"}}
+{"time":"2026-04-21T15:58:35.568146642+02:00","level":"INFO","msg":"server: parent process exited, terminating service process"}
diff --git a/wandb/run-20260421_155831-vt5kivvj/logs/debug-internal.log b/wandb/run-20260421_155831-vt5kivvj/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..fc22c38ed8f09c3d7672a15b5c192ff1d81b5dcc
--- /dev/null
+++ b/wandb/run-20260421_155831-vt5kivvj/logs/debug-internal.log
@@ -0,0 +1,10 @@
+{"time":"2026-04-21T15:58:32.280924087+02:00","level":"INFO","msg":"wandb-core"}
+{"time":"2026-04-21T15:58:32.281501604+02:00","level":"INFO","msg":"stream: starting","core version":"0.26.0"}
+{"time":"2026-04-21T15:58:32.56342524+02:00","level":"INFO","msg":"stream: created new stream","id":"vt5kivvj"}
+{"time":"2026-04-21T15:58:32.563505984+02:00","level":"INFO","msg":"handler: started"}
+{"time":"2026-04-21T15:58:32.563772869+02:00","level":"INFO","msg":"stream: started"}
+{"time":"2026-04-21T15:58:32.563903044+02:00","level":"INFO","msg":"sender: started"}
+{"time":"2026-04-21T15:58:32.563946992+02:00","level":"INFO","msg":"writer: started","stream_id":"vt5kivvj"}
+{"time":"2026-04-21T15:58:32.982336669+02:00","level":"INFO","msg":"filestream: sending request","total_files":1,"console_offset":0,"console_lines":1}
+{"time":"2026-04-21T15:58:32.986276079+02:00","level":"INFO","msg":"stream: closing"}
+{"time":"2026-04-21T15:58:33.320423065+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
diff --git a/wandb/run-20260421_155831-vt5kivvj/logs/debug.log b/wandb/run-20260421_155831-vt5kivvj/logs/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..1a8cf66b6bc29ff3a179426c09a94d0f17bb98fa
--- /dev/null
+++ b/wandb/run-20260421_155831-vt5kivvj/logs/debug.log
@@ -0,0 +1,137 @@
+2026-04-21 15:58:31,998 INFO    MainThread:210492 [wandb_setup.py:_flush():81] Current SDK version is 0.26.0
+2026-04-21 15:58:31,998 INFO    MainThread:210492 [wandb_setup.py:_flush():81] Configure stats pid to 210492
+2026-04-21 15:58:31,998 INFO    MainThread:210492 [wandb_setup.py:_flush():81] Loading settings from environment variables
+2026-04-21 15:58:31,998 INFO    MainThread:210492 [wandb_init.py:setup_run_log_directory():721] Logging user logs to /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/wandb/run-20260421_155831-vt5kivvj/logs/debug.log
+2026-04-21 15:58:31,998 INFO    MainThread:210492 [wandb_init.py:setup_run_log_directory():722] Logging internal logs to /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/wandb/run-20260421_155831-vt5kivvj/logs/debug-internal.log
+2026-04-21 15:58:32,019 INFO    MainThread:210492 [wandb_init.py:init():848] calling init triggers
+2026-04-21 15:58:32,019 INFO    MainThread:210492 [wandb_init.py:init():853] wandb.init called with sweep_config: {}
+config: {'exp_name': 'ppo', 'gym_id': 'CartPole-v1', 'learning_rate': 0.00025, 'seed': 1, 'total_timesteps': 25000, 'torch_deterministic': True, 'cuda': True, 'track': True, 'wandb_project_name': 'cleanRL', 'wandb_entity': None, 'num_envs': 4, 'capture_video': True, 'num_steps': 128, 'anneal_lr': True, 'gae': True, 'gamma': 0.99, 'gae_lambda': 0.95, 'num_minibatches': 4, 'update_epochs': 4, 'norm_adv': True, 'clip_coef': 0.2, 'clip_vloss': True, 'ent_coef': 0.01, 'vf_coef': 0.5, 'max_grad_norm': 0.5, 'target_kl': None, 'batch_size': 512, 'minibatch_size': 128, '_wandb': {'code_path': 'code/ppo.py'}}
+2026-04-21 15:58:32,019 INFO    MainThread:210492 [wandb_init.py:init():896] starting backend
+2026-04-21 15:58:32,252 INFO    MainThread:210492 [wandb_init.py:init():911] sending inform_init request
+2026-04-21 15:58:32,279 INFO    MainThread:210492 [wandb_init.py:init():919] backend started and connected
+2026-04-21 15:58:32,291 INFO    MainThread:210492 [wandb_init.py:init():989] updated telemetry
+2026-04-21 15:58:32,305 INFO    MainThread:210492 [wandb_init.py:init():1013] communicating run to backend with 90.0 second timeout
+2026-04-21 15:58:32,917 INFO    MainThread:210492 [wandb_init.py:init():1058] starting run threads in backend
+2026-04-21 15:58:32,967 INFO    MainThread:210492 [wandb_run.py:_console_start():2542] atexit reg
+2026-04-21 15:58:32,968 INFO    MainThread:210492 [wandb_run.py:_redirect():2391] redirect: wrap_raw
+2026-04-21 15:58:32,968 INFO    MainThread:210492 [wandb_run.py:_redirect():2460] Wrapping output streams.
+2026-04-21 15:58:32,968 INFO    MainThread:210492 [wandb_run.py:_redirect():2483] Redirects installed.
+2026-04-21 15:58:32,970 INFO    MainThread:210492 [wandb_init.py:init():1098] run started, returning control to user process
+2026-04-21 15:58:32,971 INFO    MainThread:210492 [wandb_run.py:_tensorboard_callback():1632] tensorboard callback: runs/CartPole-v1_ppo_1_1776779911, True
+2026-04-21 15:58:32,986 INFO    wandb-AsyncioManager-main:210492 [service_client.py:_forward_responses():134] Reached EOF.
+2026-04-21 15:58:32,986 INFO    wandb-AsyncioManager-main:210492 [mailbox.py:close():155] Closing mailbox, abandoning 1 handles.
+2026-04-21 15:58:35,188 ERROR   wandb-AsyncioManager-main:210492 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 45, in publish
+    await self._send_server_request(request)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 87, in _send_server_request
+    await self._drain_writer()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 104, in _drain_writer
+    await self._writer.drain()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 387, in drain
+    await self._protocol._drain_helper()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 190, in _drain_helper
+    raise ConnectionResetError('Connection lost')
+ConnectionResetError: Connection lost
+2026-04-21 15:58:35,192 ERROR   wandb-AsyncioManager-main:210492 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 45, in publish
+    await self._send_server_request(request)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 87, in _send_server_request
+    await self._drain_writer()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 104, in _drain_writer
+    await self._writer.drain()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 387, in drain
+    await self._protocol._drain_helper()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 190, in _drain_helper
+    raise ConnectionResetError('Connection lost')
+ConnectionResetError: Connection lost
+2026-04-21 15:58:35,194 ERROR   wandb-AsyncioManager-main:210492 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 45, in publish
+    await self._send_server_request(request)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 78, in _send_server_request
+    raise self._broken_exc.with_traceback(self._broken_tb)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 87, in _send_server_request
+    await self._drain_writer()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 104, in _drain_writer
+    await self._writer.drain()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 387, in drain
+    await self._protocol._drain_helper()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 190, in _drain_helper
+    raise ConnectionResetError('Connection lost')
+ConnectionResetError: Connection lost
+2026-04-21 15:58:35,194 ERROR   wandb-AsyncioManager-main:210492 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 45, in publish
+    await self._send_server_request(request)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 78, in _send_server_request
+    raise self._broken_exc.with_traceback(self._broken_tb)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 87, in _send_server_request
+    await self._drain_writer()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 104, in _drain_writer
+    await self._writer.drain()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 387, in drain
+    await self._protocol._drain_helper()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 190, in _drain_helper
+    raise ConnectionResetError('Connection lost')
+ConnectionResetError: Connection lost
+2026-04-21 15:58:35,195 ERROR   wandb-AsyncioManager-main:210492 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 45, in publish
+    await self._send_server_request(request)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 78, in _send_server_request
+    raise self._broken_exc.with_traceback(self._broken_tb)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 87, in _send_server_request
+    await self._drain_writer()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 104, in _drain_writer
+    await self._writer.drain()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 387, in drain
+    await self._protocol._drain_helper()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 190, in _drain_helper
+    raise ConnectionResetError('Connection lost')
+ConnectionResetError: Connection lost
+2026-04-21 15:58:35,195 ERROR   wandb-AsyncioManager-main:210492 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 45, in publish
+    await self._send_server_request(request)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 78, in _send_server_request
+    raise self._broken_exc.with_traceback(self._broken_tb)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 87, in _send_server_request
+    await self._drain_writer()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 104, in _drain_writer
+    await self._writer.drain()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 387, in drain
+    await self._protocol._drain_helper()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 190, in _drain_helper
+    raise ConnectionResetError('Connection lost')
+ConnectionResetError: Connection lost
+2026-04-21 15:58:35,195 ERROR   wandb-AsyncioManager-main:210492 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 45, in publish
+    await self._send_server_request(request)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 78, in _send_server_request
+    raise self._broken_exc.with_traceback(self._broken_tb)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 87, in _send_server_request
+    await self._drain_writer()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 104, in _drain_writer
+    await self._writer.drain()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 387, in drain
+    await self._protocol._drain_helper()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 190, in _drain_helper
+    raise ConnectionResetError('Connection lost')
+ConnectionResetError: Connection lost
diff --git a/wandb/run-20260421_155831-vt5kivvj/run-vt5kivvj.wandb b/wandb/run-20260421_155831-vt5kivvj/run-vt5kivvj.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..30977402b08eb894463b83d7de7a007255879a32
Binary files /dev/null and b/wandb/run-20260421_155831-vt5kivvj/run-vt5kivvj.wandb differ
diff --git a/wandb/run-20260421_155851-x5ptnidm/files/code/ppo.py b/wandb/run-20260421_155851-x5ptnidm/files/code/ppo.py
new file mode 100644
index 0000000000000000000000000000000000000000..29673f194c1e81ddfa6bf33c96f6cfd822f8473a
--- /dev/null
+++ b/wandb/run-20260421_155851-x5ptnidm/files/code/ppo.py
@@ -0,0 +1,313 @@
+import argparse
+import os
+from distutils.util import strtobool
+import time
+import random
+import numpy as np
+import gym
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.distributions.categorical import Categorical
+from torch.utils.tensorboard import SummaryWriter
+
+def make_env(gym_id, seed, idx, capture_video, run_name):
+    def thunk():
+        env = gym.make(gym_id)
+        env = gym.wrappers.RecordEpisodeStatistics(env)
+        if capture_video:
+            if idx == 0:
+                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", record_video_trigger=lambda t: t % 1000 == 0)
+        env.seed(seed)
+        env.action_space.seed(seed)
+        env.observation_space.seed(seed)
+        return env
+    return thunk
+
+def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
+    torch.nn.init.orthogonal_(layer.weight, std)
+    torch.nn.init.constant_(layer.bias, bias_const)
+    return layer
+
+class Agent(nn.Module):
+    def __init__(self, envs):
+        super(Agent, self).__init__()
+        self.critic = nn.Sequential(
+            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,1), std=1.),
+        )
+
+        self.actor = nn.Sequential(
+            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
+        )
+
+    def get_value(self, x):
+        return self.critic(x)
+    
+    def get_action_and_value(self, x, action=None):
+        logits = self.actor(x)
+        probs = Categorical(logits=logits)
+        if action is None:
+            action = probs.sample()
+        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+        help='the name of this experiment')
+    parser.add_argument('--gym-id', type=str, default="CartPole-v1",
+        help='the id of the gym environment')
+    parser.add_argument('--learning-rate', type=float, default=2.5e-4,
+        help='the learning rate of the optimizer')
+    parser.add_argument('--seed', type=int, default=1,
+        help='seed of the experiment')
+    parser.add_argument('--total-timesteps', type=int, default=25000,
+        help='total timesteps of the experiments')
+    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+    parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, cuda will not be enabled by default')
+    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
+        help='if toggled, this experiment will be tracked with Weights and Biases')
+    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
+        help="the wandb's project name")
+    parser.add_argument('--wandb-entity', type=str, default=None,
+        help="the entity (team) of wandb's project")
+    parser.add_argument("--num-envs", type=int, default=4,
+    help="the number of parallel game environments")
+    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="whether to capture videos of the agent performances (check out `videos` folder)")
+    parser.add_argument("--num-steps", type=int, default=128,
+        help="the number of steps to run in each environment per policy rollout")
+    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggle learning rate annealing for policy and value networks")  
+    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Use GAE for advantage computation")  
+    parser.add_argument('--gamma', type=float, default=0.99,
+        help='the discount factor gamma')
+    parser.add_argument('--gae-lambda', type=float, default=0.95,
+        help='the lambda for the general advantage estimation')
+    parser.add_argument("--num-minibatches", type=int, default=4,
+        help="the number of mini-batches")
+    parser.add_argument("--update-epochs", type=int, default=4,
+        help="the K epochs to update the policy")
+    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggles advantages normalization")
+    parser.add_argument('--clip-coef', type=float, default=0.2,
+        help='the surrogate clipping coefficient')     
+    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
+    parser.add_argument('--ent-coef', type=float, default=0.01,
+        help='coefficient of the entropy')
+    parser.add_argument('--vf-coef', type=float, default=0.5,
+        help='coefficient of the value function')
+    parser.add_argument('--max-grad-norm', type=float, default=0.5,
+        help='the maximum norm for the gradient clipping')
+    parser.add_argument('--target-kl', type=float, default=None,
+        help='the target KL divergence threshold')
+    args = parser.parse_args()
+    args.batch_size = int(args.num_envs * args.num_steps)
+    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+    return args
+
+if __name__ == "__main__":
+    args = parse_args()
+    print(args)
+    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+    if args.track:
+        import wandb
+
+        wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            sync_tensorboard=True,
+            config=vars(args),
+            name=run_name,
+            monitor_gym=True,
+            save_code=True,
+        )
+    writer =SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
+    )
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
+
+    envs = gym.vector.SyncVectorEnv(
+        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
+    for i in range(args.num_envs)])
+    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
+
+    agent = Agent(envs).to(device)
+    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
+    
+    
+    # ALGO Logic: Storage setup
+    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
+    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
+    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
+
+    #TRY NOT TO MODIFY: start the game
+    global_step = 0
+    start_time =time.time()
+    next_obs = torch.Tensor(envs.reset()).to(device)
+    next_done = torch.zeros(args.num_envs).to(device)
+    num_updates = args.total_timesteps // args.batch_size
+
+    for update in range(1, num_updates+1):
+        # Annealing the rate if instructed to do so.abs
+        if args.anneal_lr:
+            frac = 1.0 -(update -1.0) / num_updates
+            lrnow = frac * args.learning_rate
+            optimizer.param_groups[0]["lr"]=lrnow
+        
+        for step in range(0, args.num_steps):
+            global_step += 1 * args.num_envs
+            obs[step] = next_obs
+            dones[step] = next_done
+
+            # ALGO LOGIC : action logic
+            with torch.no_grad():
+                action, logprob, _, value = agent.get_action_and_value(next_obs)
+                values[step] = value.flatten()
+            actions[step] = action
+            logprobs[step] = logprob
+
+            # TRY NOT TO MODIFY: execute the game and log data.abs
+            next_obs, reward, done, info = envs.step(action.cpu().numpy())
+            rewards[step] = torch.tensor(reward).to(device).view(-1)
+            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
+        
+            if isinstance(info, dict) and "episode" in info:
+                for item in info["episode"]:
+                    if item is not None:
+                        print(f"global_step={global_step}, episodic_return={item['r']}")
+                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
+                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
+                        break
+            
+            #bootstrap reward if not done
+            with torch.no_grad():
+                next_value = agent.get_value(next_obs).reshape(1,-1)
+                if args.gae:
+                    advantages = torch.zeros_like(rewards).to(device)
+                    lastgaelam = 0
+                    for t in reversed(range(args.num_steps)):
+                        if t == args.num_steps - 1:
+                            nextnonterminal = 1.0 - next_done
+                            nextvalues = next_value
+                        else:
+                            nextnonterminal = 1.0 - dones[t+1]
+                            nextvalues = values[t+1]
+                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
+                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
+                    returns =advantages + values
+                else:
+                    returns =torch.zeros_like(rewards).to(device)
+                    for t in reversed(range(args.num_steps)):
+                        if t== args.num_steps - 1:
+                            nextnonterminal = 1.0 -next_done
+                            next_return = next_value
+                        else:
+                            nextnonterminal = 1.0 - dones[t+1]
+                            next_return = returns[t+1]
+                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
+                    advantages = returns - values
+            
+            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
+            b_logprobs =logprobs.reshape(-1)
+            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
+            b_advantages =advantages.reshape(-1)
+            b_returns = returns.reshape(-1)
+            b_values = values.reshape(-1)
+
+            # Optimizaing the policy and value network
+            b_inds = np.arange(args.batch_size)
+            clipfracs = []
+            for epoch in range(args.update_epochs):
+                np.random.shuffle(b_inds)
+                for start in range(0,args.batch_size, args.minibatch_size):
+                    end = start + args.minibatch_size
+                    mb_inds = b_inds[start:end]
+
+                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
+                        b_obs[mb_inds], b_actions.long()[mb_inds]
+                    )
+                    logratio = newlogprob - b_logprobs[mb_inds]
+                    ratio = logratio.exp()
+
+                    with torch.no_grad():
+                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
+                        old_approx_kl = (-logratio).mean()
+                        approx_kl =((ratio-1) - logratio).mean()
+                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
+
+                    mb_advantages = b_advantages[mb_inds]
+                    if args.norm_adv:
+                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
+
+                    # Policy loss
+                    pg_loss1 = -mb_advantages * ratio
+                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
+                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
+
+                    # Value loss
+                    newvalue = newvalue.view(-1)
+                    if args.clip_vloss:
+                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
+                        v_clipped = b_values[mb_inds] + torch.clamp(
+                            newvalue - b_values[mb_inds],
+                            -args.clip_coef,
+                            args.clip_coef
+                        )
+                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
+                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
+                        v_loss = 0.5 * v_loss_max.mean()
+                    else:
+                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
+                    
+                    # Entropy loss
+                    entropy_loss = entropy.mean()
+                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
+
+                    optimizer.zero_grad()
+                    loss.backward()
+                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
+                    optimizer.step()
+            
+                if args.target_kl is not None:
+                    if approx_kl > args.target_kl:
+                        break
+
+        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
+        var_y = np.var(y_true)
+        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
+
+        # TRY NOT TO MODIFY: record rewards for plotting purposes
+        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
+        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
+        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
+        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
+        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
+        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
+        writer.add_scalar("losses/explained_variance",explained_var, global_step)
+        print("SPS:", int(global_step / (time.time() - start_time)))
+        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
+
+    envs.close()
+    writer.close()
\ No newline at end of file
diff --git a/wandb/run-20260421_155851-x5ptnidm/files/config.yaml b/wandb/run-20260421_155851-x5ptnidm/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e27c794d54112b1e4f867a0a20540630e0929edf
--- /dev/null
+++ b/wandb/run-20260421_155851-x5ptnidm/files/config.yaml
@@ -0,0 +1,102 @@
+_wandb:
+    value:
+        cli_version: 0.26.0
+        code_path: code/ppo.py
+        e:
+            pjuhk7ko8vjo2id8zp3l27jhsuools2f:
+                args:
+                    - --track
+                    - --capture-video
+                codePath: ppo.py
+                codePathLocal: ppo.py
+                cpu_count: 12
+                cpu_count_logical: 14
+                disk:
+                    /:
+                        total: "499680116736"
+                        used: "141834514432"
+                email: loisfernandezcmp@gmail.com
+                executable: /home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/bin/python
+                git:
+                    commit: e1dc52c8934ce180fef18fbdf53a72281f134a52
+                    remote: https://github.com/loisonchambers/Unit-8-RL-course.git
+                host: BL-LFERNANDEZ.local.eurecat.org
+                memory:
+                    total: "33076023296"
+                os: Linux-6.17.0-20-generic-x86_64-with-glibc2.39
+                program: /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py
+                python: CPython 3.9.5
+                root: /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8
+                startedAt: "2026-04-21T13:58:51.657969Z"
+                writerId: pjuhk7ko8vjo2id8zp3l27jhsuools2f
+        m: []
+        python_version: 3.9.5
+        t:
+            "1":
+                - 1
+            "2":
+                - 1
+            "3":
+                - 13
+                - 16
+                - 35
+            "4": 3.9.5
+            "5": 0.26.0
+            "12": 0.26.0
+            "13": linux-x86_64
+anneal_lr:
+    value: true
+batch_size:
+    value: 512
+capture_video:
+    value: true
+clip_coef:
+    value: 0.2
+clip_vloss:
+    value: true
+cuda:
+    value: true
+ent_coef:
+    value: 0.01
+exp_name:
+    value: ppo
+gae:
+    value: true
+gae_lambda:
+    value: 0.95
+gamma:
+    value: 0.99
+gym_id:
+    value: CartPole-v1
+learning_rate:
+    value: 0.00025
+max_grad_norm:
+    value: 0.5
+minibatch_size:
+    value: 128
+norm_adv:
+    value: true
+num_envs:
+    value: 4
+num_minibatches:
+    value: 4
+num_steps:
+    value: 128
+seed:
+    value: 1
+target_kl:
+    value: null
+torch_deterministic:
+    value: true
+total_timesteps:
+    value: 25000
+track:
+    value: true
+update_epochs:
+    value: 4
+vf_coef:
+    value: 0.5
+wandb_entity:
+    value: null
+wandb_project_name:
+    value: cleanRL
diff --git a/wandb/run-20260421_155851-x5ptnidm/files/diff.patch b/wandb/run-20260421_155851-x5ptnidm/files/diff.patch
new file mode 100644
index 0000000000000000000000000000000000000000..d17cd83238939e160b32c123d97d1823c21c2c56
--- /dev/null
+++ b/wandb/run-20260421_155851-x5ptnidm/files/diff.patch
@@ -0,0 +1,1316 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..b2a42e5 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
++    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"},
++    {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"},
++]
++
++[[package]]
++name = "charset-normalizer"
++version = "3.4.7"
++description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "charset_normalizer-3.4.7-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:cdd68a1fb318e290a2077696b7eb7a21a49163c455979c639bf5a5dcdc46617d"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e17b8d5d6a8c47c85e68ca8379def1303fd360c3e22093a807cd34a71cd082b8"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:511ef87c8aec0783e08ac18565a16d435372bc1ac25a91e6ac7f5ef2b0bff790"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:007d05ec7321d12a40227aae9e2bc6dca73f3cb21058999a1df9e193555a9dcc"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:cf29836da5119f3c8a8a70667b0ef5fdca3bb12f80fd06487cfa575b3909b393"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux_2_31_armv7l.whl", hash = "sha256:12d8baf840cc7889b37c7c770f478adea7adce3dcb3944d02ec87508e2dcf153"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:d560742f3c0d62afaccf9f41fe485ed69bd7661a241f86a3ef0f0fb8b1a397af"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:b14b2d9dac08e28bb8046a1a0434b1750eb221c8f5b87a68f4fa11a6f97b5e34"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_armv7l.whl", hash = "sha256:bc17a677b21b3502a21f66a8cc64f5bfad4df8a0b8434d661666f8ce90ac3af1"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:750e02e074872a3fad7f233b47734166440af3cdea0add3e95163110816d6752"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_riscv64.whl", hash = "sha256:4e5163c14bffd570ef2affbfdd77bba66383890797df43dc8b4cc7d6f500bf53"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:6ed74185b2db44f41ef35fd1617c5888e59792da9bbc9190d6c7300617182616"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:94e1885b270625a9a828c9793b4d52a64445299baa1fea5a173bf1d3dd9a1a5a"},
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++]
++
++[[package]]
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++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "eval_type_backport-0.3.1-py3-none-any.whl", hash = "sha256:279ab641905e9f11129f56a8a78f493518515b83402b860f6f06dd7c011fdfa8"},
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++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++smmap = ">=3.0.1,<6"
++
++[[package]]
++name = "gitpython"
++version = "3.1.46"
++description = "GitPython is a Python library used to interact with Git repositories"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++gitdb = ">=4.0.1,<5"
++typing-extensions = {version = ">=3.10.0.2", markers = "python_version < \"3.10\""}
++
++[package.extras]
++doc = ["sphinx (>=7.1.2,<7.2)", "sphinx-autodoc-typehints", "sphinx_rtd_theme"]
++test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy (==1.18.2)", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
++
+ [[package]]
+ name = "grpcio"
+ version = "1.80.0"
+@@ -303,6 +524,45 @@ typing-extensions = ">=4.12,<5.0"
+ [package.extras]
+ protobuf = ["grpcio-tools (>=1.80.0)"]
+ 
++[[package]]
++name = "gym"
++version = "0.26.2"
++description = "Gym: A universal API for reinforcement learning environments"
++optional = false
++python-versions = ">=3.6"
++files = [
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++]
++
++[package.dependencies]
++cloudpickle = ">=1.2.0"
++gym_notices = ">=0.0.4"
++importlib_metadata = {version = ">=4.8.0", markers = "python_version < \"3.10\""}
++numpy = ">=1.18.0"
++
++[package.extras]
++accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"]
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++classic-control = ["pygame (==2.1.0)"]
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++mujoco-py = ["mujoco_py (>=2.1,<2.2)"]
++other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-python (>=3.0)"]
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++toy-text = ["pygame (==2.1.0)"]
++
++[[package]]
++name = "gym-notices"
++version = "0.1.0"
++description = "Notices for gym"
++optional = false
++python-versions = "*"
++files = [
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++]
++
+ [[package]]
+ name = "gymnasium"
+ version = "0.29.1"
+@@ -334,6 +594,36 @@ other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-pyt
+ testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
+ toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+ 
++[[package]]
++name = "idna"
++version = "3.11"
++description = "Internationalized Domain Names in Applications (IDNA)"
++optional = false
++python-versions = ">=3.8"
++files = [
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++
++[package.extras]
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++
++[[package]]
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++python-versions = ">=3.9"
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++
+ [[package]]
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+ version = "8.7.1"
+@@ -538,56 +828,39 @@ test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
+ 
+ [[package]]
+ name = "numpy"
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++version = "1.23.5"
++description = "NumPy is the fundamental package for array computing with Python."
+ optional = false
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++python-versions = ">=3.8"
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++files = [
++    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
++brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"]
++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:1ece94a2a5eda1d0e3a2d8a2fd28aa0187705d6efa5ac4c0b8680083583b7ec1"},
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:92f6f303fe2af50e3f711833a835150f9b4d8082874bfd9868cf15491ea2947e"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:a17aae051a31831388cff880251c1b5bc38fbf6a283a0ee7c543709e8e9633d1"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:28a14ffc014e523498d077dfde12839b7be586ca8c3190e72e7167c1aea6ee4c"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1995,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "f05e4899a94176c110f2df2a85ded5c10bb7d8072f071a35c990cfb2d15b58f0"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..29673f1 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -2,8 +2,62 @@ import argparse
+ import os
+ from distutils.util import strtobool
+ import time
++import random
++import numpy as np
++import gym
++import torch
++import torch.nn as nn
++import torch.optim as optim
++from torch.distributions.categorical import Categorical
+ from torch.utils.tensorboard import SummaryWriter
+ 
++def make_env(gym_id, seed, idx, capture_video, run_name):
++    def thunk():
++        env = gym.make(gym_id)
++        env = gym.wrappers.RecordEpisodeStatistics(env)
++        if capture_video:
++            if idx == 0:
++                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", record_video_trigger=lambda t: t % 1000 == 0)
++        env.seed(seed)
++        env.action_space.seed(seed)
++        env.observation_space.seed(seed)
++        return env
++    return thunk
++
++def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
++    torch.nn.init.orthogonal_(layer.weight, std)
++    torch.nn.init.constant_(layer.bias, bias_const)
++    return layer
++
++class Agent(nn.Module):
++    def __init__(self, envs):
++        super(Agent, self).__init__()
++        self.critic = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,1), std=1.),
++        )
++
++        self.actor = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
++        )
++
++    def get_value(self, x):
++        return self.critic(x)
++    
++    def get_action_and_value(self, x, action=None):
++        logits = self.actor(x)
++        probs = Categorical(logits=logits)
++        if action is None:
++            action = probs.sample()
++        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
++
+ def parse_args():
+     parser = argparse.ArgumentParser()
+     parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+@@ -16,21 +70,244 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
++    parser.add_argument("--num-envs", type=int, default=4,
++    help="the number of parallel game environments")
++    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
++        help="whether to capture videos of the agent performances (check out `videos` folder)")
++    parser.add_argument("--num-steps", type=int, default=128,
++        help="the number of steps to run in each environment per policy rollout")
++    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggle learning rate annealing for policy and value networks")  
++    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Use GAE for advantage computation")  
++    parser.add_argument('--gamma', type=float, default=0.99,
++        help='the discount factor gamma')
++    parser.add_argument('--gae-lambda', type=float, default=0.95,
++        help='the lambda for the general advantage estimation')
++    parser.add_argument("--num-minibatches", type=int, default=4,
++        help="the number of mini-batches")
++    parser.add_argument("--update-epochs", type=int, default=4,
++        help="the K epochs to update the policy")
++    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles advantages normalization")
++    parser.add_argument('--clip-coef', type=float, default=0.2,
++        help='the surrogate clipping coefficient')     
++    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
++    parser.add_argument('--ent-coef', type=float, default=0.01,
++        help='coefficient of the entropy')
++    parser.add_argument('--vf-coef', type=float, default=0.5,
++        help='coefficient of the value function')
++    parser.add_argument('--max-grad-norm', type=float, default=0.5,
++        help='the maximum norm for the gradient clipping')
++    parser.add_argument('--target-kl', type=float, default=None,
++        help='the target KL divergence threshold')
+     args = parser.parse_args()
++    args.batch_size = int(args.num_envs * args.num_steps)
++    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    envs = gym.vector.SyncVectorEnv(
++        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
++    for i in range(args.num_envs)])
++    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
++
++    agent = Agent(envs).to(device)
++    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
++    
++    
++    # ALGO Logic: Storage setup
++    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
++    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
++    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
++
++    #TRY NOT TO MODIFY: start the game
++    global_step = 0
++    start_time =time.time()
++    next_obs = torch.Tensor(envs.reset()).to(device)
++    next_done = torch.zeros(args.num_envs).to(device)
++    num_updates = args.total_timesteps // args.batch_size
++
++    for update in range(1, num_updates+1):
++        # Annealing the rate if instructed to do so.abs
++        if args.anneal_lr:
++            frac = 1.0 -(update -1.0) / num_updates
++            lrnow = frac * args.learning_rate
++            optimizer.param_groups[0]["lr"]=lrnow
++        
++        for step in range(0, args.num_steps):
++            global_step += 1 * args.num_envs
++            obs[step] = next_obs
++            dones[step] = next_done
++
++            # ALGO LOGIC : action logic
++            with torch.no_grad():
++                action, logprob, _, value = agent.get_action_and_value(next_obs)
++                values[step] = value.flatten()
++            actions[step] = action
++            logprobs[step] = logprob
++
++            # TRY NOT TO MODIFY: execute the game and log data.abs
++            next_obs, reward, done, info = envs.step(action.cpu().numpy())
++            rewards[step] = torch.tensor(reward).to(device).view(-1)
++            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
++        
++            if isinstance(info, dict) and "episode" in info:
++                for item in info["episode"]:
++                    if item is not None:
++                        print(f"global_step={global_step}, episodic_return={item['r']}")
++                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
++                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
++                        break
++            
++            #bootstrap reward if not done
++            with torch.no_grad():
++                next_value = agent.get_value(next_obs).reshape(1,-1)
++                if args.gae:
++                    advantages = torch.zeros_like(rewards).to(device)
++                    lastgaelam = 0
++                    for t in reversed(range(args.num_steps)):
++                        if t == args.num_steps - 1:
++                            nextnonterminal = 1.0 - next_done
++                            nextvalues = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            nextvalues = values[t+1]
++                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
++                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
++                    returns =advantages + values
++                else:
++                    returns =torch.zeros_like(rewards).to(device)
++                    for t in reversed(range(args.num_steps)):
++                        if t== args.num_steps - 1:
++                            nextnonterminal = 1.0 -next_done
++                            next_return = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            next_return = returns[t+1]
++                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
++                    advantages = returns - values
++            
++            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
++            b_logprobs =logprobs.reshape(-1)
++            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
++            b_advantages =advantages.reshape(-1)
++            b_returns = returns.reshape(-1)
++            b_values = values.reshape(-1)
++
++            # Optimizaing the policy and value network
++            b_inds = np.arange(args.batch_size)
++            clipfracs = []
++            for epoch in range(args.update_epochs):
++                np.random.shuffle(b_inds)
++                for start in range(0,args.batch_size, args.minibatch_size):
++                    end = start + args.minibatch_size
++                    mb_inds = b_inds[start:end]
++
++                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
++                        b_obs[mb_inds], b_actions.long()[mb_inds]
++                    )
++                    logratio = newlogprob - b_logprobs[mb_inds]
++                    ratio = logratio.exp()
++
++                    with torch.no_grad():
++                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
++                        old_approx_kl = (-logratio).mean()
++                        approx_kl =((ratio-1) - logratio).mean()
++                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
++
++                    mb_advantages = b_advantages[mb_inds]
++                    if args.norm_adv:
++                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
++
++                    # Policy loss
++                    pg_loss1 = -mb_advantages * ratio
++                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
++                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
++
++                    # Value loss
++                    newvalue = newvalue.view(-1)
++                    if args.clip_vloss:
++                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
++                        v_clipped = b_values[mb_inds] + torch.clamp(
++                            newvalue - b_values[mb_inds],
++                            -args.clip_coef,
++                            args.clip_coef
++                        )
++                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
++                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
++                        v_loss = 0.5 * v_loss_max.mean()
++                    else:
++                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
++                    
++                    # Entropy loss
++                    entropy_loss = entropy.mean()
++                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
++
++                    optimizer.zero_grad()
++                    loss.backward()
++                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
++                    optimizer.step()
++            
++                if args.target_kl is not None:
++                    if approx_kl > args.target_kl:
++                        break
++
++        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
++        var_y = np.var(y_true)
++        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
++
++        # TRY NOT TO MODIFY: record rewards for plotting purposes
++        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
++        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
++        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
++        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
++        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
++        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
++        writer.add_scalar("losses/explained_variance",explained_var, global_step)
++        print("SPS:", int(global_step / (time.time() - start_time)))
++        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
++
++    envs.close()
++    writer.close()
+\ No newline at end of file
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..b618832 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.26.2"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_155851-x5ptnidm/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch b/wandb/run-20260421_155851-x5ptnidm/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
new file mode 100644
index 0000000000000000000000000000000000000000..d17cd83238939e160b32c123d97d1823c21c2c56
--- /dev/null
+++ b/wandb/run-20260421_155851-x5ptnidm/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
@@ -0,0 +1,1316 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..b2a42e5 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
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++]
++
++[[package]]
++name = "click"
++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2"},
++    {file = "click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a"},
++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "eval_type_backport-0.3.1-py3-none-any.whl", hash = "sha256:279ab641905e9f11129f56a8a78f493518515b83402b860f6f06dd7c011fdfa8"},
++    {file = "eval_type_backport-0.3.1.tar.gz", hash = "sha256:57e993f7b5b69d271e37482e62f74e76a0276c82490cf8e4f0dffeb6b332d5ed"},
++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
++optional = false
++python-versions = ">=3.7"
++files = [
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++    {file = "gitdb-4.0.12.tar.gz", hash = "sha256:5ef71f855d191a3326fcfbc0d5da835f26b13fbcba60c32c21091c349ffdb571"},
++]
++
++[package.dependencies]
++smmap = ">=3.0.1,<6"
++
++[[package]]
++name = "gitpython"
++version = "3.1.46"
++description = "GitPython is a Python library used to interact with Git repositories"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "gitpython-3.1.46-py3-none-any.whl", hash = "sha256:79812ed143d9d25b6d176a10bb511de0f9c67b1fa641d82097b0ab90398a2058"},
++    {file = "gitpython-3.1.46.tar.gz", hash = "sha256:400124c7d0ef4ea03f7310ac2fbf7151e09ff97f2a3288d64a440c584a29c37f"},
++]
++
++[package.dependencies]
++gitdb = ">=4.0.1,<5"
++typing-extensions = {version = ">=3.10.0.2", markers = "python_version < \"3.10\""}
++
++[package.extras]
++doc = ["sphinx (>=7.1.2,<7.2)", "sphinx-autodoc-typehints", "sphinx_rtd_theme"]
++test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy (==1.18.2)", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
++
+ [[package]]
+ name = "grpcio"
+ version = "1.80.0"
+@@ -303,6 +524,45 @@ typing-extensions = ">=4.12,<5.0"
+ [package.extras]
+ protobuf = ["grpcio-tools (>=1.80.0)"]
+ 
++[[package]]
++name = "gym"
++version = "0.26.2"
++description = "Gym: A universal API for reinforcement learning environments"
++optional = false
++python-versions = ">=3.6"
++files = [
++    {file = "gym-0.26.2.tar.gz", hash = "sha256:e0d882f4b54f0c65f203104c24ab8a38b039f1289986803c7d02cdbe214fbcc4"},
++]
++
++[package.dependencies]
++cloudpickle = ">=1.2.0"
++gym_notices = ">=0.0.4"
++importlib_metadata = {version = ">=4.8.0", markers = "python_version < \"3.10\""}
++numpy = ">=1.18.0"
++
++[package.extras]
++accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"]
++all = ["ale-py (>=0.8.0,<0.9.0)", "box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "mujoco (==2.2)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)", "swig (==4.*)"]
++atari = ["ale-py (>=0.8.0,<0.9.0)"]
++box2d = ["box2d-py (==2.3.5)", "pygame (==2.1.0)", "swig (==4.*)"]
++classic-control = ["pygame (==2.1.0)"]
++mujoco = ["imageio (>=2.14.1)", "mujoco (==2.2)"]
++mujoco-py = ["mujoco_py (>=2.1,<2.2)"]
++other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-python (>=3.0)"]
++testing = ["box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "mujoco (==2.2)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)", "swig (==4.*)"]
++toy-text = ["pygame (==2.1.0)"]
++
++[[package]]
++name = "gym-notices"
++version = "0.1.0"
++description = "Notices for gym"
++optional = false
++python-versions = "*"
++files = [
++    {file = "gym_notices-0.1.0-py3-none-any.whl", hash = "sha256:a943af4446cb619d04fd1e470b9272b4473e08a06d1c7cc9005755a4a0b8c905"},
++    {file = "gym_notices-0.1.0.tar.gz", hash = "sha256:9f9477ef68a8c15e42625d4fa53631237e3e6ae947f325b5c149c081499adc1b"},
++]
++
+ [[package]]
+ name = "gymnasium"
+ version = "0.29.1"
+@@ -334,6 +594,36 @@ other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-pyt
+ testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
+ toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+ 
++[[package]]
++name = "idna"
++version = "3.11"
++description = "Internationalized Domain Names in Applications (IDNA)"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "idna-3.11-py3-none-any.whl", hash = "sha256:771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea"},
++    {file = "idna-3.11.tar.gz", hash = "sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902"},
++]
++
++[package.extras]
++all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
++
++[[package]]
++name = "imageio-ffmpeg"
++version = "0.6.0"
++description = "FFMPEG wrapper for Python"
++optional = false
++python-versions = ">=3.9"
++files = [
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++    {file = "imageio_ffmpeg-0.6.0-py3-none-win_amd64.whl", hash = "sha256:02fa47c83703c37df6bfe4896aab339013f62bf02c5ebf2dce6da56af04ffc0a"},
++    {file = "imageio_ffmpeg-0.6.0.tar.gz", hash = "sha256:e2556bed8e005564a9f925bb7afa4002d82770d6b08825078b7697ab88ba1755"},
++]
++
+ [[package]]
+ name = "importlib-metadata"
+ version = "8.7.1"
+@@ -538,56 +828,39 @@ test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
+ 
+ [[package]]
+ name = "numpy"
+-version = "2.0.2"
+-description = "Fundamental package for array computing in Python"
++version = "1.23.5"
++description = "NumPy is the fundamental package for array computing with Python."
+ optional = false
+-python-versions = ">=3.9"
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++starlette = ["starlette (>=0.19.1)"]
++starlite = ["starlite (>=1.48)"]
++statsig = ["statsig (>=0.55.3)"]
++tornado = ["tornado (>=6)"]
++unleash = ["UnleashClient (>=6.0.1)"]
++
++[[package]]
++name = "setuptools"
++version = "65.5.0"
++description = "Easily download, build, install, upgrade, and uninstall Python packages"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "setuptools-65.5.0-py3-none-any.whl", hash = "sha256:f62ea9da9ed6289bfe868cd6845968a2c854d1427f8548d52cae02a42b4f0356"},
++    {file = "setuptools-65.5.0.tar.gz", hash = "sha256:512e5536220e38146176efb833d4a62aa726b7bbff82cfbc8ba9eaa3996e0b17"},
++]
++
++[package.extras]
++docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
++testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
++testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
++
++[[package]]
++name = "smmap"
++version = "5.0.3"
++description = "A pure Python implementation of a sliding window memory map manager"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "smmap-5.0.3-py3-none-any.whl", hash = "sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f"},
++    {file = "smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c"},
++]
+ 
+ [[package]]
+ name = "sympy"
+@@ -1197,6 +1881,81 @@ files = [
+     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+ ]
+ 
++[[package]]
++name = "typing-inspection"
++version = "0.4.2"
++description = "Runtime typing introspection tools"
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
++    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
++]
++
++[package.dependencies]
++typing-extensions = ">=4.12.0"
++
++[[package]]
++name = "urllib3"
++version = "2.6.3"
++description = "HTTP library with thread-safe connection pooling, file post, and more."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
++brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"]
++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:1ece94a2a5eda1d0e3a2d8a2fd28aa0187705d6efa5ac4c0b8680083583b7ec1"},
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:92f6f303fe2af50e3f711833a835150f9b4d8082874bfd9868cf15491ea2947e"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:a17aae051a31831388cff880251c1b5bc38fbf6a283a0ee7c543709e8e9633d1"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:28a14ffc014e523498d077dfde12839b7be586ca8c3190e72e7167c1aea6ee4c"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1995,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "f05e4899a94176c110f2df2a85ded5c10bb7d8072f071a35c990cfb2d15b58f0"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..29673f1 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -2,8 +2,62 @@ import argparse
+ import os
+ from distutils.util import strtobool
+ import time
++import random
++import numpy as np
++import gym
++import torch
++import torch.nn as nn
++import torch.optim as optim
++from torch.distributions.categorical import Categorical
+ from torch.utils.tensorboard import SummaryWriter
+ 
++def make_env(gym_id, seed, idx, capture_video, run_name):
++    def thunk():
++        env = gym.make(gym_id)
++        env = gym.wrappers.RecordEpisodeStatistics(env)
++        if capture_video:
++            if idx == 0:
++                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", record_video_trigger=lambda t: t % 1000 == 0)
++        env.seed(seed)
++        env.action_space.seed(seed)
++        env.observation_space.seed(seed)
++        return env
++    return thunk
++
++def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
++    torch.nn.init.orthogonal_(layer.weight, std)
++    torch.nn.init.constant_(layer.bias, bias_const)
++    return layer
++
++class Agent(nn.Module):
++    def __init__(self, envs):
++        super(Agent, self).__init__()
++        self.critic = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,1), std=1.),
++        )
++
++        self.actor = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
++        )
++
++    def get_value(self, x):
++        return self.critic(x)
++    
++    def get_action_and_value(self, x, action=None):
++        logits = self.actor(x)
++        probs = Categorical(logits=logits)
++        if action is None:
++            action = probs.sample()
++        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
++
+ def parse_args():
+     parser = argparse.ArgumentParser()
+     parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+@@ -16,21 +70,244 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
++    parser.add_argument("--num-envs", type=int, default=4,
++    help="the number of parallel game environments")
++    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
++        help="whether to capture videos of the agent performances (check out `videos` folder)")
++    parser.add_argument("--num-steps", type=int, default=128,
++        help="the number of steps to run in each environment per policy rollout")
++    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggle learning rate annealing for policy and value networks")  
++    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Use GAE for advantage computation")  
++    parser.add_argument('--gamma', type=float, default=0.99,
++        help='the discount factor gamma')
++    parser.add_argument('--gae-lambda', type=float, default=0.95,
++        help='the lambda for the general advantage estimation')
++    parser.add_argument("--num-minibatches", type=int, default=4,
++        help="the number of mini-batches")
++    parser.add_argument("--update-epochs", type=int, default=4,
++        help="the K epochs to update the policy")
++    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles advantages normalization")
++    parser.add_argument('--clip-coef', type=float, default=0.2,
++        help='the surrogate clipping coefficient')     
++    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
++    parser.add_argument('--ent-coef', type=float, default=0.01,
++        help='coefficient of the entropy')
++    parser.add_argument('--vf-coef', type=float, default=0.5,
++        help='coefficient of the value function')
++    parser.add_argument('--max-grad-norm', type=float, default=0.5,
++        help='the maximum norm for the gradient clipping')
++    parser.add_argument('--target-kl', type=float, default=None,
++        help='the target KL divergence threshold')
+     args = parser.parse_args()
++    args.batch_size = int(args.num_envs * args.num_steps)
++    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    envs = gym.vector.SyncVectorEnv(
++        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
++    for i in range(args.num_envs)])
++    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
++
++    agent = Agent(envs).to(device)
++    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
++    
++    
++    # ALGO Logic: Storage setup
++    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
++    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
++    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
++
++    #TRY NOT TO MODIFY: start the game
++    global_step = 0
++    start_time =time.time()
++    next_obs = torch.Tensor(envs.reset()).to(device)
++    next_done = torch.zeros(args.num_envs).to(device)
++    num_updates = args.total_timesteps // args.batch_size
++
++    for update in range(1, num_updates+1):
++        # Annealing the rate if instructed to do so.abs
++        if args.anneal_lr:
++            frac = 1.0 -(update -1.0) / num_updates
++            lrnow = frac * args.learning_rate
++            optimizer.param_groups[0]["lr"]=lrnow
++        
++        for step in range(0, args.num_steps):
++            global_step += 1 * args.num_envs
++            obs[step] = next_obs
++            dones[step] = next_done
++
++            # ALGO LOGIC : action logic
++            with torch.no_grad():
++                action, logprob, _, value = agent.get_action_and_value(next_obs)
++                values[step] = value.flatten()
++            actions[step] = action
++            logprobs[step] = logprob
++
++            # TRY NOT TO MODIFY: execute the game and log data.abs
++            next_obs, reward, done, info = envs.step(action.cpu().numpy())
++            rewards[step] = torch.tensor(reward).to(device).view(-1)
++            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
++        
++            if isinstance(info, dict) and "episode" in info:
++                for item in info["episode"]:
++                    if item is not None:
++                        print(f"global_step={global_step}, episodic_return={item['r']}")
++                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
++                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
++                        break
++            
++            #bootstrap reward if not done
++            with torch.no_grad():
++                next_value = agent.get_value(next_obs).reshape(1,-1)
++                if args.gae:
++                    advantages = torch.zeros_like(rewards).to(device)
++                    lastgaelam = 0
++                    for t in reversed(range(args.num_steps)):
++                        if t == args.num_steps - 1:
++                            nextnonterminal = 1.0 - next_done
++                            nextvalues = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            nextvalues = values[t+1]
++                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
++                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
++                    returns =advantages + values
++                else:
++                    returns =torch.zeros_like(rewards).to(device)
++                    for t in reversed(range(args.num_steps)):
++                        if t== args.num_steps - 1:
++                            nextnonterminal = 1.0 -next_done
++                            next_return = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            next_return = returns[t+1]
++                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
++                    advantages = returns - values
++            
++            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
++            b_logprobs =logprobs.reshape(-1)
++            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
++            b_advantages =advantages.reshape(-1)
++            b_returns = returns.reshape(-1)
++            b_values = values.reshape(-1)
++
++            # Optimizaing the policy and value network
++            b_inds = np.arange(args.batch_size)
++            clipfracs = []
++            for epoch in range(args.update_epochs):
++                np.random.shuffle(b_inds)
++                for start in range(0,args.batch_size, args.minibatch_size):
++                    end = start + args.minibatch_size
++                    mb_inds = b_inds[start:end]
++
++                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
++                        b_obs[mb_inds], b_actions.long()[mb_inds]
++                    )
++                    logratio = newlogprob - b_logprobs[mb_inds]
++                    ratio = logratio.exp()
++
++                    with torch.no_grad():
++                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
++                        old_approx_kl = (-logratio).mean()
++                        approx_kl =((ratio-1) - logratio).mean()
++                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
++
++                    mb_advantages = b_advantages[mb_inds]
++                    if args.norm_adv:
++                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
++
++                    # Policy loss
++                    pg_loss1 = -mb_advantages * ratio
++                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
++                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
++
++                    # Value loss
++                    newvalue = newvalue.view(-1)
++                    if args.clip_vloss:
++                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
++                        v_clipped = b_values[mb_inds] + torch.clamp(
++                            newvalue - b_values[mb_inds],
++                            -args.clip_coef,
++                            args.clip_coef
++                        )
++                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
++                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
++                        v_loss = 0.5 * v_loss_max.mean()
++                    else:
++                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
++                    
++                    # Entropy loss
++                    entropy_loss = entropy.mean()
++                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
++
++                    optimizer.zero_grad()
++                    loss.backward()
++                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
++                    optimizer.step()
++            
++                if args.target_kl is not None:
++                    if approx_kl > args.target_kl:
++                        break
++
++        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
++        var_y = np.var(y_true)
++        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
++
++        # TRY NOT TO MODIFY: record rewards for plotting purposes
++        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
++        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
++        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
++        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
++        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
++        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
++        writer.add_scalar("losses/explained_variance",explained_var, global_step)
++        print("SPS:", int(global_step / (time.time() - start_time)))
++        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
++
++    envs.close()
++    writer.close()
+\ No newline at end of file
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..b618832 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.26.2"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_155851-x5ptnidm/files/output.log b/wandb/run-20260421_155851-x5ptnidm/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..157c6bc2c887541c3c5985c7127e673bd96345fd
--- /dev/null
+++ b/wandb/run-20260421_155851-x5ptnidm/files/output.log
@@ -0,0 +1,26 @@
+Exception ignored in: <function RecordVideo.__del__ at 0x71c65ee33d30>
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/record_video.py", line 211, in __del__
+    self.close_video_recorder()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/record_video.py", line 178, in close_video_recorder
+    if self.recording:
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py", line 241, in __getattr__
+    return getattr(self.env, name)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py", line 241, in __getattr__
+    return getattr(self.env, name)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py", line 241, in __getattr__
+    return getattr(self.env, name)
+  [Previous line repeated 988 more times]
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py", line 239, in __getattr__
+    if name.startswith("_"):
+RecursionError: maximum recursion depth exceeded while calling a Python object
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py", line 148, in <module>
+    envs = gym.vector.SyncVectorEnv(
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/vector/sync_vector_env.py", line 52, in __init__
+    self.envs = [env_fn() for env_fn in env_fns]
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/vector/sync_vector_env.py", line 52, in <listcomp>
+    self.envs = [env_fn() for env_fn in env_fns]
+  File "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py", line 20, in thunk
+    env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", record_video_trigger=lambda t: t % 1000 == 0)
+TypeError: __init__() got an unexpected keyword argument 'record_video_trigger'
diff --git a/wandb/run-20260421_155851-x5ptnidm/files/requirements.txt b/wandb/run-20260421_155851-x5ptnidm/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a18727d8c95d796af1d287f4f34c504091fea6f
--- /dev/null
+++ b/wandb/run-20260421_155851-x5ptnidm/files/requirements.txt
@@ -0,0 +1,70 @@
+mpmath==1.3.0
+nvidia-nvtx-cu12==12.6.77
+pluggy==1.6.0
+gym==0.26.2
+charset-normalizer==3.4.7
+Markdown==3.9
+protobuf==6.33.6
+Werkzeug==3.1.8
+gym-notices==0.1.0
+tomli==2.4.1
+GitPython==3.1.46
+nvidia-cuda-cupti-cu12==12.6.80
+MarkupSafe==3.0.3
+certifi==2026.2.25
+nvidia-cublas-cu12==12.6.4.1
+nvidia-cusparselt-cu12==0.6.3
+networkx==3.2.1
+wandb==0.26.0
+tensorboard==2.20.0
+setuptools==65.5.0
+nvidia-cusparse-cu12==12.5.4.2
+gitdb==4.0.12
+pip==24.0
+Jinja2==3.1.6
+pydantic==2.13.3
+coverage==7.10.7
+sentry-sdk==2.58.0
+exceptiongroup==1.3.1
+sympy==1.14.0
+annotated-types==0.7.0
+absl-py==2.3.1
+smmap==5.0.3
+importlib_metadata==8.7.1
+idna==3.11
+pillow==11.3.0
+tensorboard-data-server==0.7.2
+cloudpickle==3.1.2
+platformdirs==4.4.0
+pytest==7.4.4
+nvidia-curand-cu12==10.3.7.77
+fsspec==2025.10.0
+click==8.1.8
+nvidia-cuda-nvrtc-cu12==12.6.77
+gymnasium==0.29.1
+numpy==1.23.5
+imageio-ffmpeg==0.6.0
+nvidia-nccl-cu12==2.26.2
+urllib3==2.6.3
+packaging==26.0
+zipp==3.23.1
+nvidia-cufile-cu12==1.11.1.6
+nvidia-cudnn-cu12==9.5.1.17
+triton==3.3.1
+filelock==3.19.1
+Farama-Notifications==0.0.4
+grpcio==1.80.0
+iniconfig==2.1.0
+typing_extensions==4.15.0
+PyYAML==6.0.3
+nvidia-cusolver-cu12==11.7.1.2
+nvidia-cuda-runtime-cu12==12.6.77
+pygame==2.6.1
+requests==2.32.5
+pydantic_core==2.46.3
+torch==2.7.1
+nvidia-cufft-cu12==11.3.0.4
+eval_type_backport==0.3.1
+wheel==0.38.4
+typing-inspection==0.4.2
+nvidia-nvjitlink-cu12==12.6.85
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diff --git a/wandb/run-20260421_155851-x5ptnidm/files/wandb-metadata.json b/wandb/run-20260421_155851-x5ptnidm/files/wandb-metadata.json
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,32 @@
+{
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+  "python": "CPython 3.9.5",
+  "startedAt": "2026-04-21T13:58:51.657969Z",
+  "args": [
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+  ],
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+  "codePath": "ppo.py",
+  "codePathLocal": "ppo.py",
+  "git": {
+    "remote": "https://github.com/loisonchambers/Unit-8-RL-course.git",
+    "commit": "e1dc52c8934ce180fef18fbdf53a72281f134a52"
+  },
+  "email": "loisfernandezcmp@gmail.com",
+  "root": "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8",
+  "host": "BL-LFERNANDEZ.local.eurecat.org",
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diff --git a/wandb/run-20260421_155851-x5ptnidm/logs/debug.log b/wandb/run-20260421_155851-x5ptnidm/logs/debug.log
new file mode 100644
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+2026-04-21 15:58:51,660 INFO    MainThread:210784 [wandb_setup.py:_flush():81] Configure stats pid to 210784
+2026-04-21 15:58:51,660 INFO    MainThread:210784 [wandb_setup.py:_flush():81] Loading settings from environment variables
+2026-04-21 15:58:51,660 INFO    MainThread:210784 [wandb_init.py:setup_run_log_directory():721] Logging user logs to /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/wandb/run-20260421_155851-x5ptnidm/logs/debug.log
+2026-04-21 15:58:51,660 INFO    MainThread:210784 [wandb_init.py:setup_run_log_directory():722] Logging internal logs to /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/wandb/run-20260421_155851-x5ptnidm/logs/debug-internal.log
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+2026-04-21 15:58:51,685 INFO    MainThread:210784 [wandb_init.py:init():853] wandb.init called with sweep_config: {}
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+2026-04-21 15:58:52,857 INFO    wandb-AsyncioManager-main:210784 [service_client.py:_forward_responses():134] Reached EOF.
+2026-04-21 15:58:52,857 INFO    wandb-AsyncioManager-main:210784 [mailbox.py:close():155] Closing mailbox, abandoning 1 handles.
diff --git a/wandb/run-20260421_155851-x5ptnidm/run-x5ptnidm.wandb b/wandb/run-20260421_155851-x5ptnidm/run-x5ptnidm.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..ec7d864a40777a2ef29374f598517fb4f64156d4
Binary files /dev/null and b/wandb/run-20260421_155851-x5ptnidm/run-x5ptnidm.wandb differ
diff --git a/wandb/run-20260421_155957-n107cz9i/files/code/ppo.py b/wandb/run-20260421_155957-n107cz9i/files/code/ppo.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa186c92794fb5c701f689651e6ad772ed8c2a58
--- /dev/null
+++ b/wandb/run-20260421_155957-n107cz9i/files/code/ppo.py
@@ -0,0 +1,313 @@
+import argparse
+import os
+from distutils.util import strtobool
+import time
+import random
+import numpy as np
+import gym
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.distributions.categorical import Categorical
+from torch.utils.tensorboard import SummaryWriter
+
+def make_env(gym_id, seed, idx, capture_video, run_name):
+    def thunk():
+        env = gym.make(gym_id)
+        env = gym.wrappers.RecordEpisodeStatistics(env)
+        if capture_video:
+            if idx == 0:
+                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", episode_trigger=lambda t: t % 1000 == 0)
+        env.seed(seed)
+        env.action_space.seed(seed)
+        env.observation_space.seed(seed)
+        return env
+    return thunk
+
+def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
+    torch.nn.init.orthogonal_(layer.weight, std)
+    torch.nn.init.constant_(layer.bias, bias_const)
+    return layer
+
+class Agent(nn.Module):
+    def __init__(self, envs):
+        super(Agent, self).__init__()
+        self.critic = nn.Sequential(
+            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,1), std=1.),
+        )
+
+        self.actor = nn.Sequential(
+            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
+        )
+
+    def get_value(self, x):
+        return self.critic(x)
+    
+    def get_action_and_value(self, x, action=None):
+        logits = self.actor(x)
+        probs = Categorical(logits=logits)
+        if action is None:
+            action = probs.sample()
+        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+        help='the name of this experiment')
+    parser.add_argument('--gym-id', type=str, default="CartPole-v1",
+        help='the id of the gym environment')
+    parser.add_argument('--learning-rate', type=float, default=2.5e-4,
+        help='the learning rate of the optimizer')
+    parser.add_argument('--seed', type=int, default=1,
+        help='seed of the experiment')
+    parser.add_argument('--total-timesteps', type=int, default=25000,
+        help='total timesteps of the experiments')
+    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+    parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, cuda will not be enabled by default')
+    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
+        help='if toggled, this experiment will be tracked with Weights and Biases')
+    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
+        help="the wandb's project name")
+    parser.add_argument('--wandb-entity', type=str, default=None,
+        help="the entity (team) of wandb's project")
+    parser.add_argument("--num-envs", type=int, default=4,
+    help="the number of parallel game environments")
+    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="whether to capture videos of the agent performances (check out `videos` folder)")
+    parser.add_argument("--num-steps", type=int, default=128,
+        help="the number of steps to run in each environment per policy rollout")
+    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggle learning rate annealing for policy and value networks")  
+    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Use GAE for advantage computation")  
+    parser.add_argument('--gamma', type=float, default=0.99,
+        help='the discount factor gamma')
+    parser.add_argument('--gae-lambda', type=float, default=0.95,
+        help='the lambda for the general advantage estimation')
+    parser.add_argument("--num-minibatches", type=int, default=4,
+        help="the number of mini-batches")
+    parser.add_argument("--update-epochs", type=int, default=4,
+        help="the K epochs to update the policy")
+    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggles advantages normalization")
+    parser.add_argument('--clip-coef', type=float, default=0.2,
+        help='the surrogate clipping coefficient')     
+    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
+    parser.add_argument('--ent-coef', type=float, default=0.01,
+        help='coefficient of the entropy')
+    parser.add_argument('--vf-coef', type=float, default=0.5,
+        help='coefficient of the value function')
+    parser.add_argument('--max-grad-norm', type=float, default=0.5,
+        help='the maximum norm for the gradient clipping')
+    parser.add_argument('--target-kl', type=float, default=None,
+        help='the target KL divergence threshold')
+    args = parser.parse_args()
+    args.batch_size = int(args.num_envs * args.num_steps)
+    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+    return args
+
+if __name__ == "__main__":
+    args = parse_args()
+    print(args)
+    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+    if args.track:
+        import wandb
+
+        wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            sync_tensorboard=True,
+            config=vars(args),
+            name=run_name,
+            monitor_gym=True,
+            save_code=True,
+        )
+    writer =SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
+    )
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
+
+    envs = gym.vector.SyncVectorEnv(
+        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
+    for i in range(args.num_envs)])
+    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
+
+    agent = Agent(envs).to(device)
+    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
+    
+    
+    # ALGO Logic: Storage setup
+    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
+    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
+    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
+
+    #TRY NOT TO MODIFY: start the game
+    global_step = 0
+    start_time =time.time()
+    next_obs = torch.Tensor(envs.reset()).to(device)
+    next_done = torch.zeros(args.num_envs).to(device)
+    num_updates = args.total_timesteps // args.batch_size
+
+    for update in range(1, num_updates+1):
+        # Annealing the rate if instructed to do so.abs
+        if args.anneal_lr:
+            frac = 1.0 -(update -1.0) / num_updates
+            lrnow = frac * args.learning_rate
+            optimizer.param_groups[0]["lr"]=lrnow
+        
+        for step in range(0, args.num_steps):
+            global_step += 1 * args.num_envs
+            obs[step] = next_obs
+            dones[step] = next_done
+
+            # ALGO LOGIC : action logic
+            with torch.no_grad():
+                action, logprob, _, value = agent.get_action_and_value(next_obs)
+                values[step] = value.flatten()
+            actions[step] = action
+            logprobs[step] = logprob
+
+            # TRY NOT TO MODIFY: execute the game and log data.abs
+            next_obs, reward, done, info = envs.step(action.cpu().numpy())
+            rewards[step] = torch.tensor(reward).to(device).view(-1)
+            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
+        
+            if isinstance(info, dict) and "episode" in info:
+                for item in info["episode"]:
+                    if item is not None:
+                        print(f"global_step={global_step}, episodic_return={item['r']}")
+                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
+                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
+                        break
+            
+            #bootstrap reward if not done
+            with torch.no_grad():
+                next_value = agent.get_value(next_obs).reshape(1,-1)
+                if args.gae:
+                    advantages = torch.zeros_like(rewards).to(device)
+                    lastgaelam = 0
+                    for t in reversed(range(args.num_steps)):
+                        if t == args.num_steps - 1:
+                            nextnonterminal = 1.0 - next_done
+                            nextvalues = next_value
+                        else:
+                            nextnonterminal = 1.0 - dones[t+1]
+                            nextvalues = values[t+1]
+                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
+                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
+                    returns =advantages + values
+                else:
+                    returns =torch.zeros_like(rewards).to(device)
+                    for t in reversed(range(args.num_steps)):
+                        if t== args.num_steps - 1:
+                            nextnonterminal = 1.0 -next_done
+                            next_return = next_value
+                        else:
+                            nextnonterminal = 1.0 - dones[t+1]
+                            next_return = returns[t+1]
+                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
+                    advantages = returns - values
+            
+            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
+            b_logprobs =logprobs.reshape(-1)
+            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
+            b_advantages =advantages.reshape(-1)
+            b_returns = returns.reshape(-1)
+            b_values = values.reshape(-1)
+
+            # Optimizaing the policy and value network
+            b_inds = np.arange(args.batch_size)
+            clipfracs = []
+            for epoch in range(args.update_epochs):
+                np.random.shuffle(b_inds)
+                for start in range(0,args.batch_size, args.minibatch_size):
+                    end = start + args.minibatch_size
+                    mb_inds = b_inds[start:end]
+
+                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
+                        b_obs[mb_inds], b_actions.long()[mb_inds]
+                    )
+                    logratio = newlogprob - b_logprobs[mb_inds]
+                    ratio = logratio.exp()
+
+                    with torch.no_grad():
+                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
+                        old_approx_kl = (-logratio).mean()
+                        approx_kl =((ratio-1) - logratio).mean()
+                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
+
+                    mb_advantages = b_advantages[mb_inds]
+                    if args.norm_adv:
+                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
+
+                    # Policy loss
+                    pg_loss1 = -mb_advantages * ratio
+                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
+                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
+
+                    # Value loss
+                    newvalue = newvalue.view(-1)
+                    if args.clip_vloss:
+                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
+                        v_clipped = b_values[mb_inds] + torch.clamp(
+                            newvalue - b_values[mb_inds],
+                            -args.clip_coef,
+                            args.clip_coef
+                        )
+                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
+                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
+                        v_loss = 0.5 * v_loss_max.mean()
+                    else:
+                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
+                    
+                    # Entropy loss
+                    entropy_loss = entropy.mean()
+                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
+
+                    optimizer.zero_grad()
+                    loss.backward()
+                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
+                    optimizer.step()
+            
+                if args.target_kl is not None:
+                    if approx_kl > args.target_kl:
+                        break
+
+        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
+        var_y = np.var(y_true)
+        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
+
+        # TRY NOT TO MODIFY: record rewards for plotting purposes
+        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
+        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
+        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
+        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
+        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
+        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
+        writer.add_scalar("losses/explained_variance",explained_var, global_step)
+        print("SPS:", int(global_step / (time.time() - start_time)))
+        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
+
+    envs.close()
+    writer.close()
\ No newline at end of file
diff --git a/wandb/run-20260421_155957-n107cz9i/files/config.yaml b/wandb/run-20260421_155957-n107cz9i/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1a5419cb99014d670fd1f69b0859a15ba3546b94
--- /dev/null
+++ b/wandb/run-20260421_155957-n107cz9i/files/config.yaml
@@ -0,0 +1,102 @@
+_wandb:
+    value:
+        cli_version: 0.26.0
+        code_path: code/ppo.py
+        e:
+            lt86oh4mo69mgl6ayp2s8yqx6i72u7tw:
+                args:
+                    - --track
+                    - --capture-video
+                codePath: ppo.py
+                codePathLocal: ppo.py
+                cpu_count: 12
+                cpu_count_logical: 14
+                disk:
+                    /:
+                        total: "499680116736"
+                        used: "141832077312"
+                email: loisfernandezcmp@gmail.com
+                executable: /home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/bin/python
+                git:
+                    commit: e1dc52c8934ce180fef18fbdf53a72281f134a52
+                    remote: https://github.com/loisonchambers/Unit-8-RL-course.git
+                host: BL-LFERNANDEZ.local.eurecat.org
+                memory:
+                    total: "33076023296"
+                os: Linux-6.17.0-20-generic-x86_64-with-glibc2.39
+                program: /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py
+                python: CPython 3.9.5
+                root: /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8
+                startedAt: "2026-04-21T13:59:57.809334Z"
+                writerId: lt86oh4mo69mgl6ayp2s8yqx6i72u7tw
+        m: []
+        python_version: 3.9.5
+        t:
+            "1":
+                - 1
+            "2":
+                - 1
+            "3":
+                - 13
+                - 16
+                - 35
+            "4": 3.9.5
+            "5": 0.26.0
+            "12": 0.26.0
+            "13": linux-x86_64
+anneal_lr:
+    value: true
+batch_size:
+    value: 512
+capture_video:
+    value: true
+clip_coef:
+    value: 0.2
+clip_vloss:
+    value: true
+cuda:
+    value: true
+ent_coef:
+    value: 0.01
+exp_name:
+    value: ppo
+gae:
+    value: true
+gae_lambda:
+    value: 0.95
+gamma:
+    value: 0.99
+gym_id:
+    value: CartPole-v1
+learning_rate:
+    value: 0.00025
+max_grad_norm:
+    value: 0.5
+minibatch_size:
+    value: 128
+norm_adv:
+    value: true
+num_envs:
+    value: 4
+num_minibatches:
+    value: 4
+num_steps:
+    value: 128
+seed:
+    value: 1
+target_kl:
+    value: null
+torch_deterministic:
+    value: true
+total_timesteps:
+    value: 25000
+track:
+    value: true
+update_epochs:
+    value: 4
+vf_coef:
+    value: 0.5
+wandb_entity:
+    value: null
+wandb_project_name:
+    value: cleanRL
diff --git a/wandb/run-20260421_155957-n107cz9i/files/diff.patch b/wandb/run-20260421_155957-n107cz9i/files/diff.patch
new file mode 100644
index 0000000000000000000000000000000000000000..a2daf5e84c861b7ca88c0fe79ccecb04aa314e0e
--- /dev/null
+++ b/wandb/run-20260421_155957-n107cz9i/files/diff.patch
@@ -0,0 +1,1316 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..b2a42e5 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
++    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"},
++    {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"},
++]
++
++[[package]]
++name = "charset-normalizer"
++version = "3.4.7"
++description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "charset_normalizer-3.4.7-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:cdd68a1fb318e290a2077696b7eb7a21a49163c455979c639bf5a5dcdc46617d"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e17b8d5d6a8c47c85e68ca8379def1303fd360c3e22093a807cd34a71cd082b8"},
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++]
++
++[[package]]
++name = "click"
++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2"},
++    {file = "click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a"},
++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "eval_type_backport-0.3.1-py3-none-any.whl", hash = "sha256:279ab641905e9f11129f56a8a78f493518515b83402b860f6f06dd7c011fdfa8"},
++    {file = "eval_type_backport-0.3.1.tar.gz", hash = "sha256:57e993f7b5b69d271e37482e62f74e76a0276c82490cf8e4f0dffeb6b332d5ed"},
++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++smmap = ">=3.0.1,<6"
++
++[[package]]
++name = "gitpython"
++version = "3.1.46"
++description = "GitPython is a Python library used to interact with Git repositories"
++optional = false
++python-versions = ">=3.7"
++files = [
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++    {file = "gitpython-3.1.46.tar.gz", hash = "sha256:400124c7d0ef4ea03f7310ac2fbf7151e09ff97f2a3288d64a440c584a29c37f"},
++]
++
++[package.dependencies]
++gitdb = ">=4.0.1,<5"
++typing-extensions = {version = ">=3.10.0.2", markers = "python_version < \"3.10\""}
++
++[package.extras]
++doc = ["sphinx (>=7.1.2,<7.2)", "sphinx-autodoc-typehints", "sphinx_rtd_theme"]
++test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy (==1.18.2)", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
++
+ [[package]]
+ name = "grpcio"
+ version = "1.80.0"
+@@ -303,6 +524,45 @@ typing-extensions = ">=4.12,<5.0"
+ [package.extras]
+ protobuf = ["grpcio-tools (>=1.80.0)"]
+ 
++[[package]]
++name = "gym"
++version = "0.26.2"
++description = "Gym: A universal API for reinforcement learning environments"
++optional = false
++python-versions = ">=3.6"
++files = [
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++]
++
++[package.dependencies]
++cloudpickle = ">=1.2.0"
++gym_notices = ">=0.0.4"
++importlib_metadata = {version = ">=4.8.0", markers = "python_version < \"3.10\""}
++numpy = ">=1.18.0"
++
++[package.extras]
++accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"]
++all = ["ale-py (>=0.8.0,<0.9.0)", "box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "mujoco (==2.2)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)", "swig (==4.*)"]
++atari = ["ale-py (>=0.8.0,<0.9.0)"]
++box2d = ["box2d-py (==2.3.5)", "pygame (==2.1.0)", "swig (==4.*)"]
++classic-control = ["pygame (==2.1.0)"]
++mujoco = ["imageio (>=2.14.1)", "mujoco (==2.2)"]
++mujoco-py = ["mujoco_py (>=2.1,<2.2)"]
++other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-python (>=3.0)"]
++testing = ["box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "mujoco (==2.2)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)", "swig (==4.*)"]
++toy-text = ["pygame (==2.1.0)"]
++
++[[package]]
++name = "gym-notices"
++version = "0.1.0"
++description = "Notices for gym"
++optional = false
++python-versions = "*"
++files = [
++    {file = "gym_notices-0.1.0-py3-none-any.whl", hash = "sha256:a943af4446cb619d04fd1e470b9272b4473e08a06d1c7cc9005755a4a0b8c905"},
++    {file = "gym_notices-0.1.0.tar.gz", hash = "sha256:9f9477ef68a8c15e42625d4fa53631237e3e6ae947f325b5c149c081499adc1b"},
++]
++
+ [[package]]
+ name = "gymnasium"
+ version = "0.29.1"
+@@ -334,6 +594,36 @@ other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-pyt
+ testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
+ toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+ 
++[[package]]
++name = "idna"
++version = "3.11"
++description = "Internationalized Domain Names in Applications (IDNA)"
++optional = false
++python-versions = ">=3.8"
++files = [
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++    {file = "idna-3.11.tar.gz", hash = "sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902"},
++]
++
++[package.extras]
++all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
++
++[[package]]
++name = "imageio-ffmpeg"
++version = "0.6.0"
++description = "FFMPEG wrapper for Python"
++optional = false
++python-versions = ">=3.9"
++files = [
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++]
++
+ [[package]]
+ name = "importlib-metadata"
+ version = "8.7.1"
+@@ -538,56 +828,39 @@ test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
+ 
+ [[package]]
+ name = "numpy"
+-version = "2.0.2"
+-description = "Fundamental package for array computing in Python"
++version = "1.23.5"
++description = "NumPy is the fundamental package for array computing with Python."
+ optional = false
+-python-versions = ">=3.9"
++python-versions = ">=3.8"
+ files = [
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++[package.extras]
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++testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
++testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
++
++[[package]]
++name = "smmap"
++version = "5.0.3"
++description = "A pure Python implementation of a sliding window memory map manager"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "smmap-5.0.3-py3-none-any.whl", hash = "sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f"},
++    {file = "smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c"},
++]
+ 
+ [[package]]
+ name = "sympy"
+@@ -1197,6 +1881,81 @@ files = [
+     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+ ]
+ 
++[[package]]
++name = "typing-inspection"
++version = "0.4.2"
++description = "Runtime typing introspection tools"
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
++    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
++]
++
++[package.dependencies]
++typing-extensions = ">=4.12.0"
++
++[[package]]
++name = "urllib3"
++version = "2.6.3"
++description = "HTTP library with thread-safe connection pooling, file post, and more."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
++brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"]
++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:1ece94a2a5eda1d0e3a2d8a2fd28aa0187705d6efa5ac4c0b8680083583b7ec1"},
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:92f6f303fe2af50e3f711833a835150f9b4d8082874bfd9868cf15491ea2947e"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:a17aae051a31831388cff880251c1b5bc38fbf6a283a0ee7c543709e8e9633d1"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:28a14ffc014e523498d077dfde12839b7be586ca8c3190e72e7167c1aea6ee4c"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1995,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "f05e4899a94176c110f2df2a85ded5c10bb7d8072f071a35c990cfb2d15b58f0"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..aa186c9 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -2,8 +2,62 @@ import argparse
+ import os
+ from distutils.util import strtobool
+ import time
++import random
++import numpy as np
++import gym
++import torch
++import torch.nn as nn
++import torch.optim as optim
++from torch.distributions.categorical import Categorical
+ from torch.utils.tensorboard import SummaryWriter
+ 
++def make_env(gym_id, seed, idx, capture_video, run_name):
++    def thunk():
++        env = gym.make(gym_id)
++        env = gym.wrappers.RecordEpisodeStatistics(env)
++        if capture_video:
++            if idx == 0:
++                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", episode_trigger=lambda t: t % 1000 == 0)
++        env.seed(seed)
++        env.action_space.seed(seed)
++        env.observation_space.seed(seed)
++        return env
++    return thunk
++
++def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
++    torch.nn.init.orthogonal_(layer.weight, std)
++    torch.nn.init.constant_(layer.bias, bias_const)
++    return layer
++
++class Agent(nn.Module):
++    def __init__(self, envs):
++        super(Agent, self).__init__()
++        self.critic = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,1), std=1.),
++        )
++
++        self.actor = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
++        )
++
++    def get_value(self, x):
++        return self.critic(x)
++    
++    def get_action_and_value(self, x, action=None):
++        logits = self.actor(x)
++        probs = Categorical(logits=logits)
++        if action is None:
++            action = probs.sample()
++        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
++
+ def parse_args():
+     parser = argparse.ArgumentParser()
+     parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+@@ -16,21 +70,244 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
++    parser.add_argument("--num-envs", type=int, default=4,
++    help="the number of parallel game environments")
++    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
++        help="whether to capture videos of the agent performances (check out `videos` folder)")
++    parser.add_argument("--num-steps", type=int, default=128,
++        help="the number of steps to run in each environment per policy rollout")
++    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggle learning rate annealing for policy and value networks")  
++    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Use GAE for advantage computation")  
++    parser.add_argument('--gamma', type=float, default=0.99,
++        help='the discount factor gamma')
++    parser.add_argument('--gae-lambda', type=float, default=0.95,
++        help='the lambda for the general advantage estimation')
++    parser.add_argument("--num-minibatches", type=int, default=4,
++        help="the number of mini-batches")
++    parser.add_argument("--update-epochs", type=int, default=4,
++        help="the K epochs to update the policy")
++    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles advantages normalization")
++    parser.add_argument('--clip-coef', type=float, default=0.2,
++        help='the surrogate clipping coefficient')     
++    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
++    parser.add_argument('--ent-coef', type=float, default=0.01,
++        help='coefficient of the entropy')
++    parser.add_argument('--vf-coef', type=float, default=0.5,
++        help='coefficient of the value function')
++    parser.add_argument('--max-grad-norm', type=float, default=0.5,
++        help='the maximum norm for the gradient clipping')
++    parser.add_argument('--target-kl', type=float, default=None,
++        help='the target KL divergence threshold')
+     args = parser.parse_args()
++    args.batch_size = int(args.num_envs * args.num_steps)
++    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    envs = gym.vector.SyncVectorEnv(
++        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
++    for i in range(args.num_envs)])
++    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
++
++    agent = Agent(envs).to(device)
++    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
++    
++    
++    # ALGO Logic: Storage setup
++    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
++    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
++    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
++
++    #TRY NOT TO MODIFY: start the game
++    global_step = 0
++    start_time =time.time()
++    next_obs = torch.Tensor(envs.reset()).to(device)
++    next_done = torch.zeros(args.num_envs).to(device)
++    num_updates = args.total_timesteps // args.batch_size
++
++    for update in range(1, num_updates+1):
++        # Annealing the rate if instructed to do so.abs
++        if args.anneal_lr:
++            frac = 1.0 -(update -1.0) / num_updates
++            lrnow = frac * args.learning_rate
++            optimizer.param_groups[0]["lr"]=lrnow
++        
++        for step in range(0, args.num_steps):
++            global_step += 1 * args.num_envs
++            obs[step] = next_obs
++            dones[step] = next_done
++
++            # ALGO LOGIC : action logic
++            with torch.no_grad():
++                action, logprob, _, value = agent.get_action_and_value(next_obs)
++                values[step] = value.flatten()
++            actions[step] = action
++            logprobs[step] = logprob
++
++            # TRY NOT TO MODIFY: execute the game and log data.abs
++            next_obs, reward, done, info = envs.step(action.cpu().numpy())
++            rewards[step] = torch.tensor(reward).to(device).view(-1)
++            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
++        
++            if isinstance(info, dict) and "episode" in info:
++                for item in info["episode"]:
++                    if item is not None:
++                        print(f"global_step={global_step}, episodic_return={item['r']}")
++                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
++                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
++                        break
++            
++            #bootstrap reward if not done
++            with torch.no_grad():
++                next_value = agent.get_value(next_obs).reshape(1,-1)
++                if args.gae:
++                    advantages = torch.zeros_like(rewards).to(device)
++                    lastgaelam = 0
++                    for t in reversed(range(args.num_steps)):
++                        if t == args.num_steps - 1:
++                            nextnonterminal = 1.0 - next_done
++                            nextvalues = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            nextvalues = values[t+1]
++                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
++                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
++                    returns =advantages + values
++                else:
++                    returns =torch.zeros_like(rewards).to(device)
++                    for t in reversed(range(args.num_steps)):
++                        if t== args.num_steps - 1:
++                            nextnonterminal = 1.0 -next_done
++                            next_return = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            next_return = returns[t+1]
++                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
++                    advantages = returns - values
++            
++            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
++            b_logprobs =logprobs.reshape(-1)
++            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
++            b_advantages =advantages.reshape(-1)
++            b_returns = returns.reshape(-1)
++            b_values = values.reshape(-1)
++
++            # Optimizaing the policy and value network
++            b_inds = np.arange(args.batch_size)
++            clipfracs = []
++            for epoch in range(args.update_epochs):
++                np.random.shuffle(b_inds)
++                for start in range(0,args.batch_size, args.minibatch_size):
++                    end = start + args.minibatch_size
++                    mb_inds = b_inds[start:end]
++
++                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
++                        b_obs[mb_inds], b_actions.long()[mb_inds]
++                    )
++                    logratio = newlogprob - b_logprobs[mb_inds]
++                    ratio = logratio.exp()
++
++                    with torch.no_grad():
++                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
++                        old_approx_kl = (-logratio).mean()
++                        approx_kl =((ratio-1) - logratio).mean()
++                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
++
++                    mb_advantages = b_advantages[mb_inds]
++                    if args.norm_adv:
++                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
++
++                    # Policy loss
++                    pg_loss1 = -mb_advantages * ratio
++                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
++                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
++
++                    # Value loss
++                    newvalue = newvalue.view(-1)
++                    if args.clip_vloss:
++                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
++                        v_clipped = b_values[mb_inds] + torch.clamp(
++                            newvalue - b_values[mb_inds],
++                            -args.clip_coef,
++                            args.clip_coef
++                        )
++                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
++                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
++                        v_loss = 0.5 * v_loss_max.mean()
++                    else:
++                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
++                    
++                    # Entropy loss
++                    entropy_loss = entropy.mean()
++                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
++
++                    optimizer.zero_grad()
++                    loss.backward()
++                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
++                    optimizer.step()
++            
++                if args.target_kl is not None:
++                    if approx_kl > args.target_kl:
++                        break
++
++        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
++        var_y = np.var(y_true)
++        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
++
++        # TRY NOT TO MODIFY: record rewards for plotting purposes
++        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
++        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
++        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
++        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
++        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
++        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
++        writer.add_scalar("losses/explained_variance",explained_var, global_step)
++        print("SPS:", int(global_step / (time.time() - start_time)))
++        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
++
++    envs.close()
++    writer.close()
+\ No newline at end of file
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..b618832 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.26.2"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_155957-n107cz9i/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch b/wandb/run-20260421_155957-n107cz9i/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
new file mode 100644
index 0000000000000000000000000000000000000000..a2daf5e84c861b7ca88c0fe79ccecb04aa314e0e
--- /dev/null
+++ b/wandb/run-20260421_155957-n107cz9i/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
@@ -0,0 +1,1316 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..b2a42e5 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
++    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"},
++    {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"},
++]
++
++[[package]]
++name = "charset-normalizer"
++version = "3.4.7"
++description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
++optional = false
++python-versions = ">=3.7"
++files = [
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++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e17b8d5d6a8c47c85e68ca8379def1303fd360c3e22093a807cd34a71cd082b8"},
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++]
++
++[[package]]
++name = "click"
++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
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++    {file = "click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a"},
++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "eval_type_backport-0.3.1-py3-none-any.whl", hash = "sha256:279ab641905e9f11129f56a8a78f493518515b83402b860f6f06dd7c011fdfa8"},
++    {file = "eval_type_backport-0.3.1.tar.gz", hash = "sha256:57e993f7b5b69d271e37482e62f74e76a0276c82490cf8e4f0dffeb6b332d5ed"},
++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
++optional = false
++python-versions = ">=3.7"
++files = [
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++    {file = "gitdb-4.0.12.tar.gz", hash = "sha256:5ef71f855d191a3326fcfbc0d5da835f26b13fbcba60c32c21091c349ffdb571"},
++]
++
++[package.dependencies]
++smmap = ">=3.0.1,<6"
++
++[[package]]
++name = "gitpython"
++version = "3.1.46"
++description = "GitPython is a Python library used to interact with Git repositories"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "gitpython-3.1.46-py3-none-any.whl", hash = "sha256:79812ed143d9d25b6d176a10bb511de0f9c67b1fa641d82097b0ab90398a2058"},
++    {file = "gitpython-3.1.46.tar.gz", hash = "sha256:400124c7d0ef4ea03f7310ac2fbf7151e09ff97f2a3288d64a440c584a29c37f"},
++]
++
++[package.dependencies]
++gitdb = ">=4.0.1,<5"
++typing-extensions = {version = ">=3.10.0.2", markers = "python_version < \"3.10\""}
++
++[package.extras]
++doc = ["sphinx (>=7.1.2,<7.2)", "sphinx-autodoc-typehints", "sphinx_rtd_theme"]
++test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy (==1.18.2)", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
++
+ [[package]]
+ name = "grpcio"
+ version = "1.80.0"
+@@ -303,6 +524,45 @@ typing-extensions = ">=4.12,<5.0"
+ [package.extras]
+ protobuf = ["grpcio-tools (>=1.80.0)"]
+ 
++[[package]]
++name = "gym"
++version = "0.26.2"
++description = "Gym: A universal API for reinforcement learning environments"
++optional = false
++python-versions = ">=3.6"
++files = [
++    {file = "gym-0.26.2.tar.gz", hash = "sha256:e0d882f4b54f0c65f203104c24ab8a38b039f1289986803c7d02cdbe214fbcc4"},
++]
++
++[package.dependencies]
++cloudpickle = ">=1.2.0"
++gym_notices = ">=0.0.4"
++importlib_metadata = {version = ">=4.8.0", markers = "python_version < \"3.10\""}
++numpy = ">=1.18.0"
++
++[package.extras]
++accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"]
++all = ["ale-py (>=0.8.0,<0.9.0)", "box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "mujoco (==2.2)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)", "swig (==4.*)"]
++atari = ["ale-py (>=0.8.0,<0.9.0)"]
++box2d = ["box2d-py (==2.3.5)", "pygame (==2.1.0)", "swig (==4.*)"]
++classic-control = ["pygame (==2.1.0)"]
++mujoco = ["imageio (>=2.14.1)", "mujoco (==2.2)"]
++mujoco-py = ["mujoco_py (>=2.1,<2.2)"]
++other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-python (>=3.0)"]
++testing = ["box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "mujoco (==2.2)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)", "swig (==4.*)"]
++toy-text = ["pygame (==2.1.0)"]
++
++[[package]]
++name = "gym-notices"
++version = "0.1.0"
++description = "Notices for gym"
++optional = false
++python-versions = "*"
++files = [
++    {file = "gym_notices-0.1.0-py3-none-any.whl", hash = "sha256:a943af4446cb619d04fd1e470b9272b4473e08a06d1c7cc9005755a4a0b8c905"},
++    {file = "gym_notices-0.1.0.tar.gz", hash = "sha256:9f9477ef68a8c15e42625d4fa53631237e3e6ae947f325b5c149c081499adc1b"},
++]
++
+ [[package]]
+ name = "gymnasium"
+ version = "0.29.1"
+@@ -334,6 +594,36 @@ other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-pyt
+ testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
+ toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+ 
++[[package]]
++name = "idna"
++version = "3.11"
++description = "Internationalized Domain Names in Applications (IDNA)"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "idna-3.11-py3-none-any.whl", hash = "sha256:771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea"},
++    {file = "idna-3.11.tar.gz", hash = "sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902"},
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++
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+ typing = ["typing-extensions"]
+ xmp = ["defusedxml"]
+ 
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++type = ["mypy (>=1.14.1)"]
++
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++beam = ["apache-beam (>=2.12)"]
++bottle = ["bottle (>=0.12.13)"]
++celery = ["celery (>=3)"]
++celery-redbeat = ["celery-redbeat (>=2)"]
++chalice = ["chalice (>=1.16.0)"]
++clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
++django = ["django (>=1.8)"]
++falcon = ["falcon (>=1.4)"]
++fastapi = ["fastapi (>=0.79.0)"]
++flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
++google-genai = ["google-genai (>=1.29.0)"]
++grpcio = ["grpcio (>=1.21.1)", "protobuf (>=3.8.0)"]
++http2 = ["httpcore[http2] (==1.*)"]
++httpx = ["httpx (>=0.16.0)"]
++huey = ["huey (>=2)"]
++huggingface-hub = ["huggingface_hub (>=0.22)"]
++langchain = ["langchain (>=0.0.210)"]
++langgraph = ["langgraph (>=0.6.6)"]
++launchdarkly = ["launchdarkly-server-sdk (>=9.8.0)"]
++litellm = ["litellm (>=1.77.5,!=1.82.7,!=1.82.8)"]
++litestar = ["litestar (>=2.0.0)"]
++loguru = ["loguru (>=0.5)"]
++mcp = ["mcp (>=1.15.0)"]
++openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
++openfeature = ["openfeature-sdk (>=0.7.1)"]
++opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
++opentelemetry-experimental = ["opentelemetry-distro"]
++opentelemetry-otlp = ["opentelemetry-distro[otlp] (>=0.35b0)"]
++pure-eval = ["asttokens", "executing", "pure_eval"]
++pydantic-ai = ["pydantic-ai (>=1.0.0)"]
++pymongo = ["pymongo (>=3.1)"]
++pyspark = ["pyspark (>=2.4.4)"]
++quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
++rq = ["rq (>=0.6)"]
++sanic = ["sanic (>=0.8)"]
++sqlalchemy = ["sqlalchemy (>=1.2)"]
++starlette = ["starlette (>=0.19.1)"]
++starlite = ["starlite (>=1.48)"]
++statsig = ["statsig (>=0.55.3)"]
++tornado = ["tornado (>=6)"]
++unleash = ["UnleashClient (>=6.0.1)"]
++
++[[package]]
++name = "setuptools"
++version = "65.5.0"
++description = "Easily download, build, install, upgrade, and uninstall Python packages"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "setuptools-65.5.0-py3-none-any.whl", hash = "sha256:f62ea9da9ed6289bfe868cd6845968a2c854d1427f8548d52cae02a42b4f0356"},
++    {file = "setuptools-65.5.0.tar.gz", hash = "sha256:512e5536220e38146176efb833d4a62aa726b7bbff82cfbc8ba9eaa3996e0b17"},
++]
++
++[package.extras]
++docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
++testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
++testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
++
++[[package]]
++name = "smmap"
++version = "5.0.3"
++description = "A pure Python implementation of a sliding window memory map manager"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "smmap-5.0.3-py3-none-any.whl", hash = "sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f"},
++    {file = "smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c"},
++]
+ 
+ [[package]]
+ name = "sympy"
+@@ -1197,6 +1881,81 @@ files = [
+     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+ ]
+ 
++[[package]]
++name = "typing-inspection"
++version = "0.4.2"
++description = "Runtime typing introspection tools"
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
++    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
++]
++
++[package.dependencies]
++typing-extensions = ">=4.12.0"
++
++[[package]]
++name = "urllib3"
++version = "2.6.3"
++description = "HTTP library with thread-safe connection pooling, file post, and more."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
++brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"]
++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:1ece94a2a5eda1d0e3a2d8a2fd28aa0187705d6efa5ac4c0b8680083583b7ec1"},
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:92f6f303fe2af50e3f711833a835150f9b4d8082874bfd9868cf15491ea2947e"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:a17aae051a31831388cff880251c1b5bc38fbf6a283a0ee7c543709e8e9633d1"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:28a14ffc014e523498d077dfde12839b7be586ca8c3190e72e7167c1aea6ee4c"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1995,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "f05e4899a94176c110f2df2a85ded5c10bb7d8072f071a35c990cfb2d15b58f0"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..aa186c9 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -2,8 +2,62 @@ import argparse
+ import os
+ from distutils.util import strtobool
+ import time
++import random
++import numpy as np
++import gym
++import torch
++import torch.nn as nn
++import torch.optim as optim
++from torch.distributions.categorical import Categorical
+ from torch.utils.tensorboard import SummaryWriter
+ 
++def make_env(gym_id, seed, idx, capture_video, run_name):
++    def thunk():
++        env = gym.make(gym_id)
++        env = gym.wrappers.RecordEpisodeStatistics(env)
++        if capture_video:
++            if idx == 0:
++                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", episode_trigger=lambda t: t % 1000 == 0)
++        env.seed(seed)
++        env.action_space.seed(seed)
++        env.observation_space.seed(seed)
++        return env
++    return thunk
++
++def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
++    torch.nn.init.orthogonal_(layer.weight, std)
++    torch.nn.init.constant_(layer.bias, bias_const)
++    return layer
++
++class Agent(nn.Module):
++    def __init__(self, envs):
++        super(Agent, self).__init__()
++        self.critic = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,1), std=1.),
++        )
++
++        self.actor = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
++        )
++
++    def get_value(self, x):
++        return self.critic(x)
++    
++    def get_action_and_value(self, x, action=None):
++        logits = self.actor(x)
++        probs = Categorical(logits=logits)
++        if action is None:
++            action = probs.sample()
++        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
++
+ def parse_args():
+     parser = argparse.ArgumentParser()
+     parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+@@ -16,21 +70,244 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
++    parser.add_argument("--num-envs", type=int, default=4,
++    help="the number of parallel game environments")
++    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
++        help="whether to capture videos of the agent performances (check out `videos` folder)")
++    parser.add_argument("--num-steps", type=int, default=128,
++        help="the number of steps to run in each environment per policy rollout")
++    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggle learning rate annealing for policy and value networks")  
++    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Use GAE for advantage computation")  
++    parser.add_argument('--gamma', type=float, default=0.99,
++        help='the discount factor gamma')
++    parser.add_argument('--gae-lambda', type=float, default=0.95,
++        help='the lambda for the general advantage estimation')
++    parser.add_argument("--num-minibatches", type=int, default=4,
++        help="the number of mini-batches")
++    parser.add_argument("--update-epochs", type=int, default=4,
++        help="the K epochs to update the policy")
++    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles advantages normalization")
++    parser.add_argument('--clip-coef', type=float, default=0.2,
++        help='the surrogate clipping coefficient')     
++    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
++    parser.add_argument('--ent-coef', type=float, default=0.01,
++        help='coefficient of the entropy')
++    parser.add_argument('--vf-coef', type=float, default=0.5,
++        help='coefficient of the value function')
++    parser.add_argument('--max-grad-norm', type=float, default=0.5,
++        help='the maximum norm for the gradient clipping')
++    parser.add_argument('--target-kl', type=float, default=None,
++        help='the target KL divergence threshold')
+     args = parser.parse_args()
++    args.batch_size = int(args.num_envs * args.num_steps)
++    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    envs = gym.vector.SyncVectorEnv(
++        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
++    for i in range(args.num_envs)])
++    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
++
++    agent = Agent(envs).to(device)
++    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
++    
++    
++    # ALGO Logic: Storage setup
++    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
++    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
++    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
++
++    #TRY NOT TO MODIFY: start the game
++    global_step = 0
++    start_time =time.time()
++    next_obs = torch.Tensor(envs.reset()).to(device)
++    next_done = torch.zeros(args.num_envs).to(device)
++    num_updates = args.total_timesteps // args.batch_size
++
++    for update in range(1, num_updates+1):
++        # Annealing the rate if instructed to do so.abs
++        if args.anneal_lr:
++            frac = 1.0 -(update -1.0) / num_updates
++            lrnow = frac * args.learning_rate
++            optimizer.param_groups[0]["lr"]=lrnow
++        
++        for step in range(0, args.num_steps):
++            global_step += 1 * args.num_envs
++            obs[step] = next_obs
++            dones[step] = next_done
++
++            # ALGO LOGIC : action logic
++            with torch.no_grad():
++                action, logprob, _, value = agent.get_action_and_value(next_obs)
++                values[step] = value.flatten()
++            actions[step] = action
++            logprobs[step] = logprob
++
++            # TRY NOT TO MODIFY: execute the game and log data.abs
++            next_obs, reward, done, info = envs.step(action.cpu().numpy())
++            rewards[step] = torch.tensor(reward).to(device).view(-1)
++            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
++        
++            if isinstance(info, dict) and "episode" in info:
++                for item in info["episode"]:
++                    if item is not None:
++                        print(f"global_step={global_step}, episodic_return={item['r']}")
++                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
++                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
++                        break
++            
++            #bootstrap reward if not done
++            with torch.no_grad():
++                next_value = agent.get_value(next_obs).reshape(1,-1)
++                if args.gae:
++                    advantages = torch.zeros_like(rewards).to(device)
++                    lastgaelam = 0
++                    for t in reversed(range(args.num_steps)):
++                        if t == args.num_steps - 1:
++                            nextnonterminal = 1.0 - next_done
++                            nextvalues = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            nextvalues = values[t+1]
++                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
++                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
++                    returns =advantages + values
++                else:
++                    returns =torch.zeros_like(rewards).to(device)
++                    for t in reversed(range(args.num_steps)):
++                        if t== args.num_steps - 1:
++                            nextnonterminal = 1.0 -next_done
++                            next_return = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            next_return = returns[t+1]
++                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
++                    advantages = returns - values
++            
++            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
++            b_logprobs =logprobs.reshape(-1)
++            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
++            b_advantages =advantages.reshape(-1)
++            b_returns = returns.reshape(-1)
++            b_values = values.reshape(-1)
++
++            # Optimizaing the policy and value network
++            b_inds = np.arange(args.batch_size)
++            clipfracs = []
++            for epoch in range(args.update_epochs):
++                np.random.shuffle(b_inds)
++                for start in range(0,args.batch_size, args.minibatch_size):
++                    end = start + args.minibatch_size
++                    mb_inds = b_inds[start:end]
++
++                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
++                        b_obs[mb_inds], b_actions.long()[mb_inds]
++                    )
++                    logratio = newlogprob - b_logprobs[mb_inds]
++                    ratio = logratio.exp()
++
++                    with torch.no_grad():
++                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
++                        old_approx_kl = (-logratio).mean()
++                        approx_kl =((ratio-1) - logratio).mean()
++                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
++
++                    mb_advantages = b_advantages[mb_inds]
++                    if args.norm_adv:
++                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
++
++                    # Policy loss
++                    pg_loss1 = -mb_advantages * ratio
++                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
++                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
++
++                    # Value loss
++                    newvalue = newvalue.view(-1)
++                    if args.clip_vloss:
++                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
++                        v_clipped = b_values[mb_inds] + torch.clamp(
++                            newvalue - b_values[mb_inds],
++                            -args.clip_coef,
++                            args.clip_coef
++                        )
++                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
++                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
++                        v_loss = 0.5 * v_loss_max.mean()
++                    else:
++                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
++                    
++                    # Entropy loss
++                    entropy_loss = entropy.mean()
++                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
++
++                    optimizer.zero_grad()
++                    loss.backward()
++                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
++                    optimizer.step()
++            
++                if args.target_kl is not None:
++                    if approx_kl > args.target_kl:
++                        break
++
++        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
++        var_y = np.var(y_true)
++        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
++
++        # TRY NOT TO MODIFY: record rewards for plotting purposes
++        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
++        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
++        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
++        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
++        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
++        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
++        writer.add_scalar("losses/explained_variance",explained_var, global_step)
++        print("SPS:", int(global_step / (time.time() - start_time)))
++        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
++
++    envs.close()
++    writer.close()
+\ No newline at end of file
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..b618832 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.26.2"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_155957-n107cz9i/files/output.log b/wandb/run-20260421_155957-n107cz9i/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..ba9c6cb4379cce47b480b8302a0dc36666475713
--- /dev/null
+++ b/wandb/run-20260421_155957-n107cz9i/files/output.log
@@ -0,0 +1,17 @@
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py", line 148, in <module>
+    envs = gym.vector.SyncVectorEnv(
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/vector/sync_vector_env.py", line 52, in __init__
+    self.envs = [env_fn() for env_fn in env_fns]
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/vector/sync_vector_env.py", line 52, in <listcomp>
+    self.envs = [env_fn() for env_fn in env_fns]
+  File "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py", line 21, in thunk
+    env.seed(seed)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py", line 241, in __getattr__
+    return getattr(self.env, name)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py", line 241, in __getattr__
+    return getattr(self.env, name)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py", line 241, in __getattr__
+    return getattr(self.env, name)
+  [Previous line repeated 2 more times]
+AttributeError: 'CartPoleEnv' object has no attribute 'seed'
diff --git a/wandb/run-20260421_155957-n107cz9i/files/requirements.txt b/wandb/run-20260421_155957-n107cz9i/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a18727d8c95d796af1d287f4f34c504091fea6f
--- /dev/null
+++ b/wandb/run-20260421_155957-n107cz9i/files/requirements.txt
@@ -0,0 +1,70 @@
+mpmath==1.3.0
+nvidia-nvtx-cu12==12.6.77
+pluggy==1.6.0
+gym==0.26.2
+charset-normalizer==3.4.7
+Markdown==3.9
+protobuf==6.33.6
+Werkzeug==3.1.8
+gym-notices==0.1.0
+tomli==2.4.1
+GitPython==3.1.46
+nvidia-cuda-cupti-cu12==12.6.80
+MarkupSafe==3.0.3
+certifi==2026.2.25
+nvidia-cublas-cu12==12.6.4.1
+nvidia-cusparselt-cu12==0.6.3
+networkx==3.2.1
+wandb==0.26.0
+tensorboard==2.20.0
+setuptools==65.5.0
+nvidia-cusparse-cu12==12.5.4.2
+gitdb==4.0.12
+pip==24.0
+Jinja2==3.1.6
+pydantic==2.13.3
+coverage==7.10.7
+sentry-sdk==2.58.0
+exceptiongroup==1.3.1
+sympy==1.14.0
+annotated-types==0.7.0
+absl-py==2.3.1
+smmap==5.0.3
+importlib_metadata==8.7.1
+idna==3.11
+pillow==11.3.0
+tensorboard-data-server==0.7.2
+cloudpickle==3.1.2
+platformdirs==4.4.0
+pytest==7.4.4
+nvidia-curand-cu12==10.3.7.77
+fsspec==2025.10.0
+click==8.1.8
+nvidia-cuda-nvrtc-cu12==12.6.77
+gymnasium==0.29.1
+numpy==1.23.5
+imageio-ffmpeg==0.6.0
+nvidia-nccl-cu12==2.26.2
+urllib3==2.6.3
+packaging==26.0
+zipp==3.23.1
+nvidia-cufile-cu12==1.11.1.6
+nvidia-cudnn-cu12==9.5.1.17
+triton==3.3.1
+filelock==3.19.1
+Farama-Notifications==0.0.4
+grpcio==1.80.0
+iniconfig==2.1.0
+typing_extensions==4.15.0
+PyYAML==6.0.3
+nvidia-cusolver-cu12==11.7.1.2
+nvidia-cuda-runtime-cu12==12.6.77
+pygame==2.6.1
+requests==2.32.5
+pydantic_core==2.46.3
+torch==2.7.1
+nvidia-cufft-cu12==11.3.0.4
+eval_type_backport==0.3.1
+wheel==0.38.4
+typing-inspection==0.4.2
+nvidia-nvjitlink-cu12==12.6.85
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new file mode 100644
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new file mode 100644
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+  "codePath": "ppo.py",
+  "codePathLocal": "ppo.py",
+  "git": {
+    "remote": "https://github.com/loisonchambers/Unit-8-RL-course.git",
+    "commit": "e1dc52c8934ce180fef18fbdf53a72281f134a52"
+  },
+  "email": "loisfernandezcmp@gmail.com",
+  "root": "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8",
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new file mode 100644
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diff --git a/wandb/run-20260421_155957-n107cz9i/logs/debug.log b/wandb/run-20260421_155957-n107cz9i/logs/debug.log
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+2026-04-21 15:59:57,812 INFO    MainThread:211152 [wandb_init.py:setup_run_log_directory():722] Logging internal logs to /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/wandb/run-20260421_155957-n107cz9i/logs/debug-internal.log
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+2026-04-21 15:59:57,841 INFO    MainThread:211152 [wandb_init.py:init():853] wandb.init called with sweep_config: {}
+config: {'exp_name': 'ppo', 'gym_id': 'CartPole-v1', 'learning_rate': 0.00025, 'seed': 1, 'total_timesteps': 25000, 'torch_deterministic': True, 'cuda': True, 'track': True, 'wandb_project_name': 'cleanRL', 'wandb_entity': None, 'num_envs': 4, 'capture_video': True, 'num_steps': 128, 'anneal_lr': True, 'gae': True, 'gamma': 0.99, 'gae_lambda': 0.95, 'num_minibatches': 4, 'update_epochs': 4, 'norm_adv': True, 'clip_coef': 0.2, 'clip_vloss': True, 'ent_coef': 0.01, 'vf_coef': 0.5, 'max_grad_norm': 0.5, 'target_kl': None, 'batch_size': 512, 'minibatch_size': 128, '_wandb': {'code_path': 'code/ppo.py'}}
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+2026-04-21 15:59:59,074 INFO    MainThread:211152 [wandb_run.py:_redirect():2483] Redirects installed.
+2026-04-21 15:59:59,078 INFO    MainThread:211152 [wandb_init.py:init():1098] run started, returning control to user process
+2026-04-21 15:59:59,079 INFO    MainThread:211152 [wandb_run.py:_tensorboard_callback():1632] tensorboard callback: runs/CartPole-v1_ppo_1_1776779996, True
+2026-04-21 15:59:59,092 INFO    wandb-AsyncioManager-main:211152 [service_client.py:_forward_responses():134] Reached EOF.
+2026-04-21 15:59:59,092 INFO    wandb-AsyncioManager-main:211152 [mailbox.py:close():155] Closing mailbox, abandoning 1 handles.
diff --git a/wandb/run-20260421_155957-n107cz9i/run-n107cz9i.wandb b/wandb/run-20260421_155957-n107cz9i/run-n107cz9i.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..c54e9902dcb91361c6ccd73785e3ee7e3afe8abc
Binary files /dev/null and b/wandb/run-20260421_155957-n107cz9i/run-n107cz9i.wandb differ
diff --git a/wandb/run-20260421_160121-lnmebn7s/files/code/ppo.py b/wandb/run-20260421_160121-lnmebn7s/files/code/ppo.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa186c92794fb5c701f689651e6ad772ed8c2a58
--- /dev/null
+++ b/wandb/run-20260421_160121-lnmebn7s/files/code/ppo.py
@@ -0,0 +1,313 @@
+import argparse
+import os
+from distutils.util import strtobool
+import time
+import random
+import numpy as np
+import gym
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.distributions.categorical import Categorical
+from torch.utils.tensorboard import SummaryWriter
+
+def make_env(gym_id, seed, idx, capture_video, run_name):
+    def thunk():
+        env = gym.make(gym_id)
+        env = gym.wrappers.RecordEpisodeStatistics(env)
+        if capture_video:
+            if idx == 0:
+                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", episode_trigger=lambda t: t % 1000 == 0)
+        env.seed(seed)
+        env.action_space.seed(seed)
+        env.observation_space.seed(seed)
+        return env
+    return thunk
+
+def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
+    torch.nn.init.orthogonal_(layer.weight, std)
+    torch.nn.init.constant_(layer.bias, bias_const)
+    return layer
+
+class Agent(nn.Module):
+    def __init__(self, envs):
+        super(Agent, self).__init__()
+        self.critic = nn.Sequential(
+            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,1), std=1.),
+        )
+
+        self.actor = nn.Sequential(
+            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
+        )
+
+    def get_value(self, x):
+        return self.critic(x)
+    
+    def get_action_and_value(self, x, action=None):
+        logits = self.actor(x)
+        probs = Categorical(logits=logits)
+        if action is None:
+            action = probs.sample()
+        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+        help='the name of this experiment')
+    parser.add_argument('--gym-id', type=str, default="CartPole-v1",
+        help='the id of the gym environment')
+    parser.add_argument('--learning-rate', type=float, default=2.5e-4,
+        help='the learning rate of the optimizer')
+    parser.add_argument('--seed', type=int, default=1,
+        help='seed of the experiment')
+    parser.add_argument('--total-timesteps', type=int, default=25000,
+        help='total timesteps of the experiments')
+    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+    parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, cuda will not be enabled by default')
+    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
+        help='if toggled, this experiment will be tracked with Weights and Biases')
+    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
+        help="the wandb's project name")
+    parser.add_argument('--wandb-entity', type=str, default=None,
+        help="the entity (team) of wandb's project")
+    parser.add_argument("--num-envs", type=int, default=4,
+    help="the number of parallel game environments")
+    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="whether to capture videos of the agent performances (check out `videos` folder)")
+    parser.add_argument("--num-steps", type=int, default=128,
+        help="the number of steps to run in each environment per policy rollout")
+    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggle learning rate annealing for policy and value networks")  
+    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Use GAE for advantage computation")  
+    parser.add_argument('--gamma', type=float, default=0.99,
+        help='the discount factor gamma')
+    parser.add_argument('--gae-lambda', type=float, default=0.95,
+        help='the lambda for the general advantage estimation')
+    parser.add_argument("--num-minibatches", type=int, default=4,
+        help="the number of mini-batches")
+    parser.add_argument("--update-epochs", type=int, default=4,
+        help="the K epochs to update the policy")
+    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggles advantages normalization")
+    parser.add_argument('--clip-coef', type=float, default=0.2,
+        help='the surrogate clipping coefficient')     
+    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
+    parser.add_argument('--ent-coef', type=float, default=0.01,
+        help='coefficient of the entropy')
+    parser.add_argument('--vf-coef', type=float, default=0.5,
+        help='coefficient of the value function')
+    parser.add_argument('--max-grad-norm', type=float, default=0.5,
+        help='the maximum norm for the gradient clipping')
+    parser.add_argument('--target-kl', type=float, default=None,
+        help='the target KL divergence threshold')
+    args = parser.parse_args()
+    args.batch_size = int(args.num_envs * args.num_steps)
+    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+    return args
+
+if __name__ == "__main__":
+    args = parse_args()
+    print(args)
+    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+    if args.track:
+        import wandb
+
+        wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            sync_tensorboard=True,
+            config=vars(args),
+            name=run_name,
+            monitor_gym=True,
+            save_code=True,
+        )
+    writer =SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
+    )
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
+
+    envs = gym.vector.SyncVectorEnv(
+        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
+    for i in range(args.num_envs)])
+    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
+
+    agent = Agent(envs).to(device)
+    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
+    
+    
+    # ALGO Logic: Storage setup
+    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
+    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
+    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
+
+    #TRY NOT TO MODIFY: start the game
+    global_step = 0
+    start_time =time.time()
+    next_obs = torch.Tensor(envs.reset()).to(device)
+    next_done = torch.zeros(args.num_envs).to(device)
+    num_updates = args.total_timesteps // args.batch_size
+
+    for update in range(1, num_updates+1):
+        # Annealing the rate if instructed to do so.abs
+        if args.anneal_lr:
+            frac = 1.0 -(update -1.0) / num_updates
+            lrnow = frac * args.learning_rate
+            optimizer.param_groups[0]["lr"]=lrnow
+        
+        for step in range(0, args.num_steps):
+            global_step += 1 * args.num_envs
+            obs[step] = next_obs
+            dones[step] = next_done
+
+            # ALGO LOGIC : action logic
+            with torch.no_grad():
+                action, logprob, _, value = agent.get_action_and_value(next_obs)
+                values[step] = value.flatten()
+            actions[step] = action
+            logprobs[step] = logprob
+
+            # TRY NOT TO MODIFY: execute the game and log data.abs
+            next_obs, reward, done, info = envs.step(action.cpu().numpy())
+            rewards[step] = torch.tensor(reward).to(device).view(-1)
+            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
+        
+            if isinstance(info, dict) and "episode" in info:
+                for item in info["episode"]:
+                    if item is not None:
+                        print(f"global_step={global_step}, episodic_return={item['r']}")
+                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
+                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
+                        break
+            
+            #bootstrap reward if not done
+            with torch.no_grad():
+                next_value = agent.get_value(next_obs).reshape(1,-1)
+                if args.gae:
+                    advantages = torch.zeros_like(rewards).to(device)
+                    lastgaelam = 0
+                    for t in reversed(range(args.num_steps)):
+                        if t == args.num_steps - 1:
+                            nextnonterminal = 1.0 - next_done
+                            nextvalues = next_value
+                        else:
+                            nextnonterminal = 1.0 - dones[t+1]
+                            nextvalues = values[t+1]
+                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
+                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
+                    returns =advantages + values
+                else:
+                    returns =torch.zeros_like(rewards).to(device)
+                    for t in reversed(range(args.num_steps)):
+                        if t== args.num_steps - 1:
+                            nextnonterminal = 1.0 -next_done
+                            next_return = next_value
+                        else:
+                            nextnonterminal = 1.0 - dones[t+1]
+                            next_return = returns[t+1]
+                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
+                    advantages = returns - values
+            
+            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
+            b_logprobs =logprobs.reshape(-1)
+            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
+            b_advantages =advantages.reshape(-1)
+            b_returns = returns.reshape(-1)
+            b_values = values.reshape(-1)
+
+            # Optimizaing the policy and value network
+            b_inds = np.arange(args.batch_size)
+            clipfracs = []
+            for epoch in range(args.update_epochs):
+                np.random.shuffle(b_inds)
+                for start in range(0,args.batch_size, args.minibatch_size):
+                    end = start + args.minibatch_size
+                    mb_inds = b_inds[start:end]
+
+                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
+                        b_obs[mb_inds], b_actions.long()[mb_inds]
+                    )
+                    logratio = newlogprob - b_logprobs[mb_inds]
+                    ratio = logratio.exp()
+
+                    with torch.no_grad():
+                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
+                        old_approx_kl = (-logratio).mean()
+                        approx_kl =((ratio-1) - logratio).mean()
+                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
+
+                    mb_advantages = b_advantages[mb_inds]
+                    if args.norm_adv:
+                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
+
+                    # Policy loss
+                    pg_loss1 = -mb_advantages * ratio
+                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
+                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
+
+                    # Value loss
+                    newvalue = newvalue.view(-1)
+                    if args.clip_vloss:
+                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
+                        v_clipped = b_values[mb_inds] + torch.clamp(
+                            newvalue - b_values[mb_inds],
+                            -args.clip_coef,
+                            args.clip_coef
+                        )
+                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
+                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
+                        v_loss = 0.5 * v_loss_max.mean()
+                    else:
+                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
+                    
+                    # Entropy loss
+                    entropy_loss = entropy.mean()
+                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
+
+                    optimizer.zero_grad()
+                    loss.backward()
+                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
+                    optimizer.step()
+            
+                if args.target_kl is not None:
+                    if approx_kl > args.target_kl:
+                        break
+
+        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
+        var_y = np.var(y_true)
+        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
+
+        # TRY NOT TO MODIFY: record rewards for plotting purposes
+        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
+        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
+        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
+        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
+        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
+        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
+        writer.add_scalar("losses/explained_variance",explained_var, global_step)
+        print("SPS:", int(global_step / (time.time() - start_time)))
+        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
+
+    envs.close()
+    writer.close()
\ No newline at end of file
diff --git a/wandb/run-20260421_160121-lnmebn7s/files/config.yaml b/wandb/run-20260421_160121-lnmebn7s/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..af46f424a6c3927b34e754a60804a776018f4167
--- /dev/null
+++ b/wandb/run-20260421_160121-lnmebn7s/files/config.yaml
@@ -0,0 +1,103 @@
+_wandb:
+    value:
+        cli_version: 0.26.0
+        code_path: code/ppo.py
+        e:
+            3jyyjzxnqpfbwgf2xcdxx12gt0nun6ho:
+                args:
+                    - --track
+                    - --capture-video
+                codePath: ppo.py
+                codePathLocal: ppo.py
+                cpu_count: 12
+                cpu_count_logical: 14
+                disk:
+                    /:
+                        total: "499680116736"
+                        used: "141830070272"
+                email: loisfernandezcmp@gmail.com
+                executable: /home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/bin/python
+                git:
+                    commit: e1dc52c8934ce180fef18fbdf53a72281f134a52
+                    remote: https://github.com/loisonchambers/Unit-8-RL-course.git
+                host: BL-LFERNANDEZ.local.eurecat.org
+                memory:
+                    total: "33076023296"
+                os: Linux-6.17.0-20-generic-x86_64-with-glibc2.39
+                program: /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py
+                python: CPython 3.9.5
+                root: /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8
+                startedAt: "2026-04-21T14:01:21.847957Z"
+                writerId: 3jyyjzxnqpfbwgf2xcdxx12gt0nun6ho
+        m: []
+        python_version: 3.9.5
+        t:
+            "1":
+                - 1
+            "2":
+                - 1
+            "3":
+                - 13
+                - 16
+                - 35
+                - 41
+            "4": 3.9.5
+            "5": 0.26.0
+            "12": 0.26.0
+            "13": linux-x86_64
+anneal_lr:
+    value: true
+batch_size:
+    value: 512
+capture_video:
+    value: true
+clip_coef:
+    value: 0.2
+clip_vloss:
+    value: true
+cuda:
+    value: true
+ent_coef:
+    value: 0.01
+exp_name:
+    value: ppo
+gae:
+    value: true
+gae_lambda:
+    value: 0.95
+gamma:
+    value: 0.99
+gym_id:
+    value: CartPole-v1
+learning_rate:
+    value: 0.00025
+max_grad_norm:
+    value: 0.5
+minibatch_size:
+    value: 128
+norm_adv:
+    value: true
+num_envs:
+    value: 4
+num_minibatches:
+    value: 4
+num_steps:
+    value: 128
+seed:
+    value: 1
+target_kl:
+    value: null
+torch_deterministic:
+    value: true
+total_timesteps:
+    value: 25000
+track:
+    value: true
+update_epochs:
+    value: 4
+vf_coef:
+    value: 0.5
+wandb_entity:
+    value: null
+wandb_project_name:
+    value: cleanRL
diff --git a/wandb/run-20260421_160121-lnmebn7s/files/diff.patch b/wandb/run-20260421_160121-lnmebn7s/files/diff.patch
new file mode 100644
index 0000000000000000000000000000000000000000..7eac201403b7a518f382a7af485673ef065e473a
--- /dev/null
+++ b/wandb/run-20260421_160121-lnmebn7s/files/diff.patch
@@ -0,0 +1,1316 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..bc1e1ab 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
++    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"},
++    {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"},
++]
++
++[[package]]
++name = "charset-normalizer"
++version = "3.4.7"
++description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[[package]]
++name = "click"
++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2"},
++    {file = "click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a"},
++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "eval_type_backport-0.3.1-py3-none-any.whl", hash = "sha256:279ab641905e9f11129f56a8a78f493518515b83402b860f6f06dd7c011fdfa8"},
++    {file = "eval_type_backport-0.3.1.tar.gz", hash = "sha256:57e993f7b5b69d271e37482e62f74e76a0276c82490cf8e4f0dffeb6b332d5ed"},
++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
++optional = false
++python-versions = ">=3.7"
++files = [
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++    {file = "gitdb-4.0.12.tar.gz", hash = "sha256:5ef71f855d191a3326fcfbc0d5da835f26b13fbcba60c32c21091c349ffdb571"},
++]
++
++[package.dependencies]
++smmap = ">=3.0.1,<6"
++
++[[package]]
++name = "gitpython"
++version = "3.1.46"
++description = "GitPython is a Python library used to interact with Git repositories"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "gitpython-3.1.46-py3-none-any.whl", hash = "sha256:79812ed143d9d25b6d176a10bb511de0f9c67b1fa641d82097b0ab90398a2058"},
++    {file = "gitpython-3.1.46.tar.gz", hash = "sha256:400124c7d0ef4ea03f7310ac2fbf7151e09ff97f2a3288d64a440c584a29c37f"},
++]
++
++[package.dependencies]
++gitdb = ">=4.0.1,<5"
++typing-extensions = {version = ">=3.10.0.2", markers = "python_version < \"3.10\""}
++
++[package.extras]
++doc = ["sphinx (>=7.1.2,<7.2)", "sphinx-autodoc-typehints", "sphinx_rtd_theme"]
++test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy (==1.18.2)", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
++
+ [[package]]
+ name = "grpcio"
+ version = "1.80.0"
+@@ -303,6 +524,45 @@ typing-extensions = ">=4.12,<5.0"
+ [package.extras]
+ protobuf = ["grpcio-tools (>=1.80.0)"]
+ 
++[[package]]
++name = "gym"
++version = "0.25.1"
++description = "Gym: A universal API for reinforcement learning environments"
++optional = false
++python-versions = ">=3.6"
++files = [
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++]
++
++[package.dependencies]
++cloudpickle = ">=1.2.0"
++gym_notices = ">=0.0.4"
++importlib_metadata = {version = ">=4.8.0", markers = "python_version < \"3.10\""}
++numpy = ">=1.18.0"
++
++[package.extras]
++accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"]
++all = ["ale-py (>=0.7.5,<0.8.0)", "box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "mujoco (==2.2.0)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)"]
++atari = ["ale-py (>=0.7.5,<0.8.0)"]
++box2d = ["box2d-py (==2.3.5)", "pygame (==2.1.0)"]
++classic-control = ["pygame (==2.1.0)"]
++mujoco = ["imageio (>=2.14.1)", "mujoco (==2.2.0)"]
++mujoco-py = ["mujoco_py (>=2.1,<2.2)"]
++other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "opencv-python (>=3.0)"]
++testing = ["box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "mujoco (==2.2.0)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)"]
++toy-text = ["pygame (==2.1.0)"]
++
++[[package]]
++name = "gym-notices"
++version = "0.1.0"
++description = "Notices for gym"
++optional = false
++python-versions = "*"
++files = [
++    {file = "gym_notices-0.1.0-py3-none-any.whl", hash = "sha256:a943af4446cb619d04fd1e470b9272b4473e08a06d1c7cc9005755a4a0b8c905"},
++    {file = "gym_notices-0.1.0.tar.gz", hash = "sha256:9f9477ef68a8c15e42625d4fa53631237e3e6ae947f325b5c149c081499adc1b"},
++]
++
+ [[package]]
+ name = "gymnasium"
+ version = "0.29.1"
+@@ -334,6 +594,36 @@ other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-pyt
+ testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
+ toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+ 
++[[package]]
++name = "idna"
++version = "3.11"
++description = "Internationalized Domain Names in Applications (IDNA)"
++optional = false
++python-versions = ">=3.8"
++files = [
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++    {file = "idna-3.11.tar.gz", hash = "sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902"},
++]
++
++[package.extras]
++all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
++
++[[package]]
++name = "imageio-ffmpeg"
++version = "0.6.0"
++description = "FFMPEG wrapper for Python"
++optional = false
++python-versions = ">=3.9"
++files = [
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++]
++
+ [[package]]
+ name = "importlib-metadata"
+ version = "8.7.1"
+@@ -538,56 +828,39 @@ test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
+ 
+ [[package]]
+ name = "numpy"
+-version = "2.0.2"
+-description = "Fundamental package for array computing in Python"
++version = "1.23.5"
++description = "NumPy is the fundamental package for array computing with Python."
+ optional = false
+-python-versions = ">=3.9"
++python-versions = ">=3.8"
+ files = [
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++
++[package.extras]
++docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
++testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
++testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
++
++[[package]]
++name = "smmap"
++version = "5.0.3"
++description = "A pure Python implementation of a sliding window memory map manager"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "smmap-5.0.3-py3-none-any.whl", hash = "sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f"},
++    {file = "smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c"},
++]
+ 
+ [[package]]
+ name = "sympy"
+@@ -1197,6 +1881,81 @@ files = [
+     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+ ]
+ 
++[[package]]
++name = "typing-inspection"
++version = "0.4.2"
++description = "Runtime typing introspection tools"
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
++    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
++]
++
++[package.dependencies]
++typing-extensions = ">=4.12.0"
++
++[[package]]
++name = "urllib3"
++version = "2.6.3"
++description = "HTTP library with thread-safe connection pooling, file post, and more."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
++brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"]
++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:1ece94a2a5eda1d0e3a2d8a2fd28aa0187705d6efa5ac4c0b8680083583b7ec1"},
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:92f6f303fe2af50e3f711833a835150f9b4d8082874bfd9868cf15491ea2947e"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:a17aae051a31831388cff880251c1b5bc38fbf6a283a0ee7c543709e8e9633d1"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:28a14ffc014e523498d077dfde12839b7be586ca8c3190e72e7167c1aea6ee4c"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1995,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "f11507e147d7c2cb9dd240bbb479024772dae6eaca788addca05b86fe9a8479c"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..aa186c9 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -2,8 +2,62 @@ import argparse
+ import os
+ from distutils.util import strtobool
+ import time
++import random
++import numpy as np
++import gym
++import torch
++import torch.nn as nn
++import torch.optim as optim
++from torch.distributions.categorical import Categorical
+ from torch.utils.tensorboard import SummaryWriter
+ 
++def make_env(gym_id, seed, idx, capture_video, run_name):
++    def thunk():
++        env = gym.make(gym_id)
++        env = gym.wrappers.RecordEpisodeStatistics(env)
++        if capture_video:
++            if idx == 0:
++                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", episode_trigger=lambda t: t % 1000 == 0)
++        env.seed(seed)
++        env.action_space.seed(seed)
++        env.observation_space.seed(seed)
++        return env
++    return thunk
++
++def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
++    torch.nn.init.orthogonal_(layer.weight, std)
++    torch.nn.init.constant_(layer.bias, bias_const)
++    return layer
++
++class Agent(nn.Module):
++    def __init__(self, envs):
++        super(Agent, self).__init__()
++        self.critic = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,1), std=1.),
++        )
++
++        self.actor = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
++        )
++
++    def get_value(self, x):
++        return self.critic(x)
++    
++    def get_action_and_value(self, x, action=None):
++        logits = self.actor(x)
++        probs = Categorical(logits=logits)
++        if action is None:
++            action = probs.sample()
++        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
++
+ def parse_args():
+     parser = argparse.ArgumentParser()
+     parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+@@ -16,21 +70,244 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
++    parser.add_argument("--num-envs", type=int, default=4,
++    help="the number of parallel game environments")
++    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
++        help="whether to capture videos of the agent performances (check out `videos` folder)")
++    parser.add_argument("--num-steps", type=int, default=128,
++        help="the number of steps to run in each environment per policy rollout")
++    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggle learning rate annealing for policy and value networks")  
++    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Use GAE for advantage computation")  
++    parser.add_argument('--gamma', type=float, default=0.99,
++        help='the discount factor gamma')
++    parser.add_argument('--gae-lambda', type=float, default=0.95,
++        help='the lambda for the general advantage estimation')
++    parser.add_argument("--num-minibatches", type=int, default=4,
++        help="the number of mini-batches")
++    parser.add_argument("--update-epochs", type=int, default=4,
++        help="the K epochs to update the policy")
++    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles advantages normalization")
++    parser.add_argument('--clip-coef', type=float, default=0.2,
++        help='the surrogate clipping coefficient')     
++    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
++    parser.add_argument('--ent-coef', type=float, default=0.01,
++        help='coefficient of the entropy')
++    parser.add_argument('--vf-coef', type=float, default=0.5,
++        help='coefficient of the value function')
++    parser.add_argument('--max-grad-norm', type=float, default=0.5,
++        help='the maximum norm for the gradient clipping')
++    parser.add_argument('--target-kl', type=float, default=None,
++        help='the target KL divergence threshold')
+     args = parser.parse_args()
++    args.batch_size = int(args.num_envs * args.num_steps)
++    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    envs = gym.vector.SyncVectorEnv(
++        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
++    for i in range(args.num_envs)])
++    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
++
++    agent = Agent(envs).to(device)
++    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
++    
++    
++    # ALGO Logic: Storage setup
++    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
++    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
++    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
++
++    #TRY NOT TO MODIFY: start the game
++    global_step = 0
++    start_time =time.time()
++    next_obs = torch.Tensor(envs.reset()).to(device)
++    next_done = torch.zeros(args.num_envs).to(device)
++    num_updates = args.total_timesteps // args.batch_size
++
++    for update in range(1, num_updates+1):
++        # Annealing the rate if instructed to do so.abs
++        if args.anneal_lr:
++            frac = 1.0 -(update -1.0) / num_updates
++            lrnow = frac * args.learning_rate
++            optimizer.param_groups[0]["lr"]=lrnow
++        
++        for step in range(0, args.num_steps):
++            global_step += 1 * args.num_envs
++            obs[step] = next_obs
++            dones[step] = next_done
++
++            # ALGO LOGIC : action logic
++            with torch.no_grad():
++                action, logprob, _, value = agent.get_action_and_value(next_obs)
++                values[step] = value.flatten()
++            actions[step] = action
++            logprobs[step] = logprob
++
++            # TRY NOT TO MODIFY: execute the game and log data.abs
++            next_obs, reward, done, info = envs.step(action.cpu().numpy())
++            rewards[step] = torch.tensor(reward).to(device).view(-1)
++            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
++        
++            if isinstance(info, dict) and "episode" in info:
++                for item in info["episode"]:
++                    if item is not None:
++                        print(f"global_step={global_step}, episodic_return={item['r']}")
++                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
++                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
++                        break
++            
++            #bootstrap reward if not done
++            with torch.no_grad():
++                next_value = agent.get_value(next_obs).reshape(1,-1)
++                if args.gae:
++                    advantages = torch.zeros_like(rewards).to(device)
++                    lastgaelam = 0
++                    for t in reversed(range(args.num_steps)):
++                        if t == args.num_steps - 1:
++                            nextnonterminal = 1.0 - next_done
++                            nextvalues = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            nextvalues = values[t+1]
++                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
++                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
++                    returns =advantages + values
++                else:
++                    returns =torch.zeros_like(rewards).to(device)
++                    for t in reversed(range(args.num_steps)):
++                        if t== args.num_steps - 1:
++                            nextnonterminal = 1.0 -next_done
++                            next_return = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            next_return = returns[t+1]
++                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
++                    advantages = returns - values
++            
++            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
++            b_logprobs =logprobs.reshape(-1)
++            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
++            b_advantages =advantages.reshape(-1)
++            b_returns = returns.reshape(-1)
++            b_values = values.reshape(-1)
++
++            # Optimizaing the policy and value network
++            b_inds = np.arange(args.batch_size)
++            clipfracs = []
++            for epoch in range(args.update_epochs):
++                np.random.shuffle(b_inds)
++                for start in range(0,args.batch_size, args.minibatch_size):
++                    end = start + args.minibatch_size
++                    mb_inds = b_inds[start:end]
++
++                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
++                        b_obs[mb_inds], b_actions.long()[mb_inds]
++                    )
++                    logratio = newlogprob - b_logprobs[mb_inds]
++                    ratio = logratio.exp()
++
++                    with torch.no_grad():
++                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
++                        old_approx_kl = (-logratio).mean()
++                        approx_kl =((ratio-1) - logratio).mean()
++                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
++
++                    mb_advantages = b_advantages[mb_inds]
++                    if args.norm_adv:
++                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
++
++                    # Policy loss
++                    pg_loss1 = -mb_advantages * ratio
++                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
++                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
++
++                    # Value loss
++                    newvalue = newvalue.view(-1)
++                    if args.clip_vloss:
++                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
++                        v_clipped = b_values[mb_inds] + torch.clamp(
++                            newvalue - b_values[mb_inds],
++                            -args.clip_coef,
++                            args.clip_coef
++                        )
++                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
++                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
++                        v_loss = 0.5 * v_loss_max.mean()
++                    else:
++                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
++                    
++                    # Entropy loss
++                    entropy_loss = entropy.mean()
++                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
++
++                    optimizer.zero_grad()
++                    loss.backward()
++                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
++                    optimizer.step()
++            
++                if args.target_kl is not None:
++                    if approx_kl > args.target_kl:
++                        break
++
++        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
++        var_y = np.var(y_true)
++        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
++
++        # TRY NOT TO MODIFY: record rewards for plotting purposes
++        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
++        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
++        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
++        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
++        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
++        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
++        writer.add_scalar("losses/explained_variance",explained_var, global_step)
++        print("SPS:", int(global_step / (time.time() - start_time)))
++        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
++
++    envs.close()
++    writer.close()
+\ No newline at end of file
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..d2ae71c 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.25.1"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_160121-lnmebn7s/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch b/wandb/run-20260421_160121-lnmebn7s/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
new file mode 100644
index 0000000000000000000000000000000000000000..7eac201403b7a518f382a7af485673ef065e473a
--- /dev/null
+++ b/wandb/run-20260421_160121-lnmebn7s/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
@@ -0,0 +1,1316 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..bc1e1ab 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
++    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"},
++    {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"},
++]
++
++[[package]]
++name = "charset-normalizer"
++version = "3.4.7"
++description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[[package]]
++name = "click"
++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
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++    {file = "click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a"},
++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "eval_type_backport-0.3.1-py3-none-any.whl", hash = "sha256:279ab641905e9f11129f56a8a78f493518515b83402b860f6f06dd7c011fdfa8"},
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++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
++optional = false
++python-versions = ">=3.7"
++files = [
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++    {file = "gitdb-4.0.12.tar.gz", hash = "sha256:5ef71f855d191a3326fcfbc0d5da835f26b13fbcba60c32c21091c349ffdb571"},
++]
++
++[package.dependencies]
++smmap = ">=3.0.1,<6"
++
++[[package]]
++name = "gitpython"
++version = "3.1.46"
++description = "GitPython is a Python library used to interact with Git repositories"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "gitpython-3.1.46-py3-none-any.whl", hash = "sha256:79812ed143d9d25b6d176a10bb511de0f9c67b1fa641d82097b0ab90398a2058"},
++    {file = "gitpython-3.1.46.tar.gz", hash = "sha256:400124c7d0ef4ea03f7310ac2fbf7151e09ff97f2a3288d64a440c584a29c37f"},
++]
++
++[package.dependencies]
++gitdb = ">=4.0.1,<5"
++typing-extensions = {version = ">=3.10.0.2", markers = "python_version < \"3.10\""}
++
++[package.extras]
++doc = ["sphinx (>=7.1.2,<7.2)", "sphinx-autodoc-typehints", "sphinx_rtd_theme"]
++test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy (==1.18.2)", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
++
+ [[package]]
+ name = "grpcio"
+ version = "1.80.0"
+@@ -303,6 +524,45 @@ typing-extensions = ">=4.12,<5.0"
+ [package.extras]
+ protobuf = ["grpcio-tools (>=1.80.0)"]
+ 
++[[package]]
++name = "gym"
++version = "0.25.1"
++description = "Gym: A universal API for reinforcement learning environments"
++optional = false
++python-versions = ">=3.6"
++files = [
++    {file = "gym-0.25.1.tar.gz", hash = "sha256:26809d60d654215956a1642bba344a765c934a0a65a5a07208caa0a97f4eb0ec"},
++]
++
++[package.dependencies]
++cloudpickle = ">=1.2.0"
++gym_notices = ">=0.0.4"
++importlib_metadata = {version = ">=4.8.0", markers = "python_version < \"3.10\""}
++numpy = ">=1.18.0"
++
++[package.extras]
++accept-rom-license = ["autorom[accept-rom-license] (>=0.4.2,<0.5.0)"]
++all = ["ale-py (>=0.7.5,<0.8.0)", "box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "mujoco (==2.2.0)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)"]
++atari = ["ale-py (>=0.7.5,<0.8.0)"]
++box2d = ["box2d-py (==2.3.5)", "pygame (==2.1.0)"]
++classic-control = ["pygame (==2.1.0)"]
++mujoco = ["imageio (>=2.14.1)", "mujoco (==2.2.0)"]
++mujoco-py = ["mujoco_py (>=2.1,<2.2)"]
++other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "opencv-python (>=3.0)"]
++testing = ["box2d-py (==2.3.5)", "imageio (>=2.14.1)", "lz4 (>=3.1.0)", "matplotlib (>=3.0)", "mujoco (==2.2.0)", "mujoco_py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (==2.1.0)", "pytest (==7.0.1)"]
++toy-text = ["pygame (==2.1.0)"]
++
++[[package]]
++name = "gym-notices"
++version = "0.1.0"
++description = "Notices for gym"
++optional = false
++python-versions = "*"
++files = [
++    {file = "gym_notices-0.1.0-py3-none-any.whl", hash = "sha256:a943af4446cb619d04fd1e470b9272b4473e08a06d1c7cc9005755a4a0b8c905"},
++    {file = "gym_notices-0.1.0.tar.gz", hash = "sha256:9f9477ef68a8c15e42625d4fa53631237e3e6ae947f325b5c149c081499adc1b"},
++]
++
+ [[package]]
+ name = "gymnasium"
+ version = "0.29.1"
+@@ -334,6 +594,36 @@ other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-pyt
+ testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
+ toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+ 
++[[package]]
++name = "idna"
++version = "3.11"
++description = "Internationalized Domain Names in Applications (IDNA)"
++optional = false
++python-versions = ">=3.8"
++files = [
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++    {file = "idna-3.11.tar.gz", hash = "sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902"},
++]
++
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+ typing = ["typing-extensions"]
+ xmp = ["defusedxml"]
+ 
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++type = ["mypy (>=1.14.1)"]
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++beam = ["apache-beam (>=2.12)"]
++bottle = ["bottle (>=0.12.13)"]
++celery = ["celery (>=3)"]
++celery-redbeat = ["celery-redbeat (>=2)"]
++chalice = ["chalice (>=1.16.0)"]
++clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
++django = ["django (>=1.8)"]
++falcon = ["falcon (>=1.4)"]
++fastapi = ["fastapi (>=0.79.0)"]
++flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
++google-genai = ["google-genai (>=1.29.0)"]
++grpcio = ["grpcio (>=1.21.1)", "protobuf (>=3.8.0)"]
++http2 = ["httpcore[http2] (==1.*)"]
++httpx = ["httpx (>=0.16.0)"]
++huey = ["huey (>=2)"]
++huggingface-hub = ["huggingface_hub (>=0.22)"]
++langchain = ["langchain (>=0.0.210)"]
++langgraph = ["langgraph (>=0.6.6)"]
++launchdarkly = ["launchdarkly-server-sdk (>=9.8.0)"]
++litellm = ["litellm (>=1.77.5,!=1.82.7,!=1.82.8)"]
++litestar = ["litestar (>=2.0.0)"]
++loguru = ["loguru (>=0.5)"]
++mcp = ["mcp (>=1.15.0)"]
++openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
++openfeature = ["openfeature-sdk (>=0.7.1)"]
++opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
++opentelemetry-experimental = ["opentelemetry-distro"]
++opentelemetry-otlp = ["opentelemetry-distro[otlp] (>=0.35b0)"]
++pure-eval = ["asttokens", "executing", "pure_eval"]
++pydantic-ai = ["pydantic-ai (>=1.0.0)"]
++pymongo = ["pymongo (>=3.1)"]
++pyspark = ["pyspark (>=2.4.4)"]
++quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
++rq = ["rq (>=0.6)"]
++sanic = ["sanic (>=0.8)"]
++sqlalchemy = ["sqlalchemy (>=1.2)"]
++starlette = ["starlette (>=0.19.1)"]
++starlite = ["starlite (>=1.48)"]
++statsig = ["statsig (>=0.55.3)"]
++tornado = ["tornado (>=6)"]
++unleash = ["UnleashClient (>=6.0.1)"]
++
++[[package]]
++name = "setuptools"
++version = "65.5.0"
++description = "Easily download, build, install, upgrade, and uninstall Python packages"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "setuptools-65.5.0-py3-none-any.whl", hash = "sha256:f62ea9da9ed6289bfe868cd6845968a2c854d1427f8548d52cae02a42b4f0356"},
++    {file = "setuptools-65.5.0.tar.gz", hash = "sha256:512e5536220e38146176efb833d4a62aa726b7bbff82cfbc8ba9eaa3996e0b17"},
++]
++
++[package.extras]
++docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
++testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
++testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
++
++[[package]]
++name = "smmap"
++version = "5.0.3"
++description = "A pure Python implementation of a sliding window memory map manager"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "smmap-5.0.3-py3-none-any.whl", hash = "sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f"},
++    {file = "smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c"},
++]
+ 
+ [[package]]
+ name = "sympy"
+@@ -1197,6 +1881,81 @@ files = [
+     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+ ]
+ 
++[[package]]
++name = "typing-inspection"
++version = "0.4.2"
++description = "Runtime typing introspection tools"
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
++    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
++]
++
++[package.dependencies]
++typing-extensions = ">=4.12.0"
++
++[[package]]
++name = "urllib3"
++version = "2.6.3"
++description = "HTTP library with thread-safe connection pooling, file post, and more."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
++brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"]
++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:1ece94a2a5eda1d0e3a2d8a2fd28aa0187705d6efa5ac4c0b8680083583b7ec1"},
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:92f6f303fe2af50e3f711833a835150f9b4d8082874bfd9868cf15491ea2947e"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:a17aae051a31831388cff880251c1b5bc38fbf6a283a0ee7c543709e8e9633d1"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:28a14ffc014e523498d077dfde12839b7be586ca8c3190e72e7167c1aea6ee4c"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1995,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "f11507e147d7c2cb9dd240bbb479024772dae6eaca788addca05b86fe9a8479c"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..aa186c9 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -2,8 +2,62 @@ import argparse
+ import os
+ from distutils.util import strtobool
+ import time
++import random
++import numpy as np
++import gym
++import torch
++import torch.nn as nn
++import torch.optim as optim
++from torch.distributions.categorical import Categorical
+ from torch.utils.tensorboard import SummaryWriter
+ 
++def make_env(gym_id, seed, idx, capture_video, run_name):
++    def thunk():
++        env = gym.make(gym_id)
++        env = gym.wrappers.RecordEpisodeStatistics(env)
++        if capture_video:
++            if idx == 0:
++                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", episode_trigger=lambda t: t % 1000 == 0)
++        env.seed(seed)
++        env.action_space.seed(seed)
++        env.observation_space.seed(seed)
++        return env
++    return thunk
++
++def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
++    torch.nn.init.orthogonal_(layer.weight, std)
++    torch.nn.init.constant_(layer.bias, bias_const)
++    return layer
++
++class Agent(nn.Module):
++    def __init__(self, envs):
++        super(Agent, self).__init__()
++        self.critic = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,1), std=1.),
++        )
++
++        self.actor = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
++        )
++
++    def get_value(self, x):
++        return self.critic(x)
++    
++    def get_action_and_value(self, x, action=None):
++        logits = self.actor(x)
++        probs = Categorical(logits=logits)
++        if action is None:
++            action = probs.sample()
++        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
++
+ def parse_args():
+     parser = argparse.ArgumentParser()
+     parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+@@ -16,21 +70,244 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
++    parser.add_argument("--num-envs", type=int, default=4,
++    help="the number of parallel game environments")
++    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
++        help="whether to capture videos of the agent performances (check out `videos` folder)")
++    parser.add_argument("--num-steps", type=int, default=128,
++        help="the number of steps to run in each environment per policy rollout")
++    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggle learning rate annealing for policy and value networks")  
++    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Use GAE for advantage computation")  
++    parser.add_argument('--gamma', type=float, default=0.99,
++        help='the discount factor gamma')
++    parser.add_argument('--gae-lambda', type=float, default=0.95,
++        help='the lambda for the general advantage estimation')
++    parser.add_argument("--num-minibatches", type=int, default=4,
++        help="the number of mini-batches")
++    parser.add_argument("--update-epochs", type=int, default=4,
++        help="the K epochs to update the policy")
++    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles advantages normalization")
++    parser.add_argument('--clip-coef', type=float, default=0.2,
++        help='the surrogate clipping coefficient')     
++    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
++    parser.add_argument('--ent-coef', type=float, default=0.01,
++        help='coefficient of the entropy')
++    parser.add_argument('--vf-coef', type=float, default=0.5,
++        help='coefficient of the value function')
++    parser.add_argument('--max-grad-norm', type=float, default=0.5,
++        help='the maximum norm for the gradient clipping')
++    parser.add_argument('--target-kl', type=float, default=None,
++        help='the target KL divergence threshold')
+     args = parser.parse_args()
++    args.batch_size = int(args.num_envs * args.num_steps)
++    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    envs = gym.vector.SyncVectorEnv(
++        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
++    for i in range(args.num_envs)])
++    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
++
++    agent = Agent(envs).to(device)
++    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
++    
++    
++    # ALGO Logic: Storage setup
++    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
++    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
++    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
++
++    #TRY NOT TO MODIFY: start the game
++    global_step = 0
++    start_time =time.time()
++    next_obs = torch.Tensor(envs.reset()).to(device)
++    next_done = torch.zeros(args.num_envs).to(device)
++    num_updates = args.total_timesteps // args.batch_size
++
++    for update in range(1, num_updates+1):
++        # Annealing the rate if instructed to do so.abs
++        if args.anneal_lr:
++            frac = 1.0 -(update -1.0) / num_updates
++            lrnow = frac * args.learning_rate
++            optimizer.param_groups[0]["lr"]=lrnow
++        
++        for step in range(0, args.num_steps):
++            global_step += 1 * args.num_envs
++            obs[step] = next_obs
++            dones[step] = next_done
++
++            # ALGO LOGIC : action logic
++            with torch.no_grad():
++                action, logprob, _, value = agent.get_action_and_value(next_obs)
++                values[step] = value.flatten()
++            actions[step] = action
++            logprobs[step] = logprob
++
++            # TRY NOT TO MODIFY: execute the game and log data.abs
++            next_obs, reward, done, info = envs.step(action.cpu().numpy())
++            rewards[step] = torch.tensor(reward).to(device).view(-1)
++            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
++        
++            if isinstance(info, dict) and "episode" in info:
++                for item in info["episode"]:
++                    if item is not None:
++                        print(f"global_step={global_step}, episodic_return={item['r']}")
++                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
++                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
++                        break
++            
++            #bootstrap reward if not done
++            with torch.no_grad():
++                next_value = agent.get_value(next_obs).reshape(1,-1)
++                if args.gae:
++                    advantages = torch.zeros_like(rewards).to(device)
++                    lastgaelam = 0
++                    for t in reversed(range(args.num_steps)):
++                        if t == args.num_steps - 1:
++                            nextnonterminal = 1.0 - next_done
++                            nextvalues = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            nextvalues = values[t+1]
++                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
++                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
++                    returns =advantages + values
++                else:
++                    returns =torch.zeros_like(rewards).to(device)
++                    for t in reversed(range(args.num_steps)):
++                        if t== args.num_steps - 1:
++                            nextnonterminal = 1.0 -next_done
++                            next_return = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            next_return = returns[t+1]
++                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
++                    advantages = returns - values
++            
++            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
++            b_logprobs =logprobs.reshape(-1)
++            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
++            b_advantages =advantages.reshape(-1)
++            b_returns = returns.reshape(-1)
++            b_values = values.reshape(-1)
++
++            # Optimizaing the policy and value network
++            b_inds = np.arange(args.batch_size)
++            clipfracs = []
++            for epoch in range(args.update_epochs):
++                np.random.shuffle(b_inds)
++                for start in range(0,args.batch_size, args.minibatch_size):
++                    end = start + args.minibatch_size
++                    mb_inds = b_inds[start:end]
++
++                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
++                        b_obs[mb_inds], b_actions.long()[mb_inds]
++                    )
++                    logratio = newlogprob - b_logprobs[mb_inds]
++                    ratio = logratio.exp()
++
++                    with torch.no_grad():
++                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
++                        old_approx_kl = (-logratio).mean()
++                        approx_kl =((ratio-1) - logratio).mean()
++                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
++
++                    mb_advantages = b_advantages[mb_inds]
++                    if args.norm_adv:
++                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
++
++                    # Policy loss
++                    pg_loss1 = -mb_advantages * ratio
++                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
++                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
++
++                    # Value loss
++                    newvalue = newvalue.view(-1)
++                    if args.clip_vloss:
++                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
++                        v_clipped = b_values[mb_inds] + torch.clamp(
++                            newvalue - b_values[mb_inds],
++                            -args.clip_coef,
++                            args.clip_coef
++                        )
++                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
++                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
++                        v_loss = 0.5 * v_loss_max.mean()
++                    else:
++                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
++                    
++                    # Entropy loss
++                    entropy_loss = entropy.mean()
++                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
++
++                    optimizer.zero_grad()
++                    loss.backward()
++                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
++                    optimizer.step()
++            
++                if args.target_kl is not None:
++                    if approx_kl > args.target_kl:
++                        break
++
++        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
++        var_y = np.var(y_true)
++        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
++
++        # TRY NOT TO MODIFY: record rewards for plotting purposes
++        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
++        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
++        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
++        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
++        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
++        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
++        writer.add_scalar("losses/explained_variance",explained_var, global_step)
++        print("SPS:", int(global_step / (time.time() - start_time)))
++        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
++
++    envs.close()
++    writer.close()
+\ No newline at end of file
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..d2ae71c 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.25.1"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_160121-lnmebn7s/files/output.log b/wandb/run-20260421_160121-lnmebn7s/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..217835d3aaa36703a4b5b451066bc4b6e303aff6
--- /dev/null
+++ b/wandb/run-20260421_160121-lnmebn7s/files/output.log
@@ -0,0 +1,194 @@
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py:329: DeprecationWarning: WARN: Initializing wrapper in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.
+  deprecation(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/step_api_compatibility.py:39: DeprecationWarning: WARN: Initializing environment in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.
+  deprecation(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py:268: DeprecationWarning: WARN: Function `env.seed(seed)` is marked as deprecated and will be removed in the future. Please use `env.reset(seed=seed)` instead.
+  deprecation(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/vector/vector_env.py:56: DeprecationWarning: WARN: Initializing vector env in old step API which returns one bool array instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.
+  deprecation(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/monitoring/video_recorder.py:78: DeprecationWarning: WARN: Recording ability for environment CartPole-v1 initialized with `render_mode=None` is marked as deprecated and will be removed in the future.
+  logger.deprecation(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py:51: DeprecationWarning: WARN: The argument mode in render method is deprecated; use render_mode during environment initialization instead.
+See here for more information: https://www.gymlibrary.ml/content/api/
+  deprecation(
+global_step=60, episodic_return=15.0
+global_step=88, episodic_return=22.0
+global_step=120, episodic_return=15.0
+global_step=132, episodic_return=11.0
+global_step=160, episodic_return=25.0
+global_step=172, episodic_return=43.0
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+global_step=200, episodic_return=17.0
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+global_step=428, episodic_return=20.0
+global_step=432, episodic_return=41.0
+global_step=496, episodic_return=16.0
+SPS: 24
+global_step=560, episodic_return=71.0
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+global_step=608, episodic_return=12.0
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+global_step=928, episodic_return=80.0
+global_step=1024, episodic_return=24.0
+SPS: 25
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diff --git a/wandb/run-20260421_160121-lnmebn7s/files/requirements.txt b/wandb/run-20260421_160121-lnmebn7s/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4fb5c94ae55ac1d721cd2c4dd82e39e1d8ead9d5
--- /dev/null
+++ b/wandb/run-20260421_160121-lnmebn7s/files/requirements.txt
@@ -0,0 +1,70 @@
+mpmath==1.3.0
+nvidia-nvtx-cu12==12.6.77
+pluggy==1.6.0
+charset-normalizer==3.4.7
+gym==0.25.1
+Markdown==3.9
+protobuf==6.33.6
+Werkzeug==3.1.8
+gym-notices==0.1.0
+tomli==2.4.1
+GitPython==3.1.46
+nvidia-cuda-cupti-cu12==12.6.80
+MarkupSafe==3.0.3
+certifi==2026.2.25
+nvidia-cublas-cu12==12.6.4.1
+nvidia-cusparselt-cu12==0.6.3
+networkx==3.2.1
+wandb==0.26.0
+tensorboard==2.20.0
+setuptools==65.5.0
+nvidia-cusparse-cu12==12.5.4.2
+gitdb==4.0.12
+pip==24.0
+Jinja2==3.1.6
+pydantic==2.13.3
+coverage==7.10.7
+sentry-sdk==2.58.0
+exceptiongroup==1.3.1
+sympy==1.14.0
+annotated-types==0.7.0
+absl-py==2.3.1
+smmap==5.0.3
+importlib_metadata==8.7.1
+idna==3.11
+pillow==11.3.0
+tensorboard-data-server==0.7.2
+cloudpickle==3.1.2
+platformdirs==4.4.0
+pytest==7.4.4
+nvidia-curand-cu12==10.3.7.77
+fsspec==2025.10.0
+click==8.1.8
+nvidia-cuda-nvrtc-cu12==12.6.77
+gymnasium==0.29.1
+numpy==1.23.5
+imageio-ffmpeg==0.6.0
+nvidia-nccl-cu12==2.26.2
+urllib3==2.6.3
+packaging==26.0
+zipp==3.23.1
+nvidia-cufile-cu12==1.11.1.6
+nvidia-cudnn-cu12==9.5.1.17
+triton==3.3.1
+filelock==3.19.1
+Farama-Notifications==0.0.4
+grpcio==1.80.0
+iniconfig==2.1.0
+typing_extensions==4.15.0
+PyYAML==6.0.3
+nvidia-cusolver-cu12==11.7.1.2
+nvidia-cuda-runtime-cu12==12.6.77
+pygame==2.6.1
+requests==2.32.5
+pydantic_core==2.46.3
+torch==2.7.1
+nvidia-cufft-cu12==11.3.0.4
+eval_type_backport==0.3.1
+wheel==0.38.4
+typing-inspection==0.4.2
+nvidia-nvjitlink-cu12==12.6.85
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new file mode 100644
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--- /dev/null
+++ b/wandb/run-20260421_160121-lnmebn7s/files/wandb-metadata.json
@@ -0,0 +1,32 @@
+{
+  "os": "Linux-6.17.0-20-generic-x86_64-with-glibc2.39",
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+  "startedAt": "2026-04-21T14:01:21.847957Z",
+  "args": [
+    "--track",
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+  ],
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+  "codePath": "ppo.py",
+  "codePathLocal": "ppo.py",
+  "git": {
+    "remote": "https://github.com/loisonchambers/Unit-8-RL-course.git",
+    "commit": "e1dc52c8934ce180fef18fbdf53a72281f134a52"
+  },
+  "email": "loisfernandezcmp@gmail.com",
+  "root": "/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8",
+  "host": "BL-LFERNANDEZ.local.eurecat.org",
+  "executable": "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/bin/python",
+  "cpu_count": 12,
+  "cpu_count_logical": 14,
+  "disk": {
+    "/": {
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+  },
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+}
\ No newline at end of file
diff --git a/wandb/run-20260421_160121-lnmebn7s/files/wandb-summary.json b/wandb/run-20260421_160121-lnmebn7s/files/wandb-summary.json
new file mode 100644
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--- /dev/null
+++ b/wandb/run-20260421_160121-lnmebn7s/files/wandb-summary.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/wandb/run-20260421_160121-lnmebn7s/logs/debug-core.log b/wandb/run-20260421_160121-lnmebn7s/logs/debug-core.log
new file mode 100644
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--- /dev/null
+++ b/wandb/run-20260421_160121-lnmebn7s/logs/debug-core.log
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+{"time":"2026-04-21T16:15:52.184217145+02:00","level":"INFO","msg":"server is shutting down"}
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+{"time":"2026-04-21T16:31:20.814335074+02:00","level":"INFO","msg":"server: parent process exited, terminating service process"}
diff --git a/wandb/run-20260421_160121-lnmebn7s/logs/debug-internal.log b/wandb/run-20260421_160121-lnmebn7s/logs/debug-internal.log
new file mode 100644
index 0000000000000000000000000000000000000000..d2e9cf61e0e7e66e6fd6cbee423834d8b1a379b7
--- /dev/null
+++ b/wandb/run-20260421_160121-lnmebn7s/logs/debug-internal.log
@@ -0,0 +1,68 @@
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+{"time":"2026-04-21T16:01:23.724793239+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
+{"time":"2026-04-21T16:01:33.135008198+02:00","level":"WARN","msg":"tensorboard: no root directory","error":"timed out after 10s"}
+{"time":"2026-04-21T16:01:33.135240477+02:00","level":"INFO","msg":"tensorboard: tracking new log directory","rootDir":{},"logDir":"LocalOrCloudPath(LocalPath=\"/home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/runs/CartPole-v1_ppo_1_1776780080\")","namespace":""}
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+{"time":"2026-04-21T16:01:38.439933252+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
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+{"time":"2026-04-21T16:01:55.973585707+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
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+{"time":"2026-04-21T16:02:08.158471112+02:00","level":"INFO","msg":"flowcontrol: unblocked","totalOffloaded":9}
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+{"time":"2026-04-21T16:02:08.497592536+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
+{"time":"2026-04-21T16:02:23.169947772+02:00","level":"INFO","msg":"filestream: sending request","total_files":4,"history_offset":70,"history_lines":6,"events_offset":3,"events_lines":1,"console_offset":42,"console_lines":5}
+{"time":"2026-04-21T16:02:43.201706319+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
+{"time":"2026-04-21T16:02:43.202377467+02:00","level":"INFO","msg":"filestream: sending request","total_files":4,"history_offset":76,"history_lines":20,"events_offset":4,"events_lines":1,"console_offset":47,"console_lines":5}
+{"time":"2026-04-21T16:02:43.468763245+02:00","level":"INFO","msg":"filestream: request sent","status":"200 OK"}
+{"time":"2026-04-21T16:02:53.169089745+02:00","level":"INFO","msg":"filestream: sending request","total_files":4,"history_offset":96,"history_lines":12,"events_offset":5,"events_lines":1,"console_offset":52,"console_lines":3}
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+{"time":"2026-04-21T16:11:59.476835708+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/files/loisonchambers-eurecat/cleanRL/lnmebn7s/file_stream\": net/http: request canceled (Client.Timeout exceeded while awaiting headers)"}
+{"time":"2026-04-21T16:15:08.947003835+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/files/loisonchambers-eurecat/cleanRL/lnmebn7s/file_stream\": net/http: request canceled (Client.Timeout exceeded while awaiting headers)"}
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+{"time":"2026-04-21T16:17:03.010954735+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": dial tcp: lookup api.wandb.ai on 127.0.0.53:53: server misbehaving"}
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+{"time":"2026-04-21T16:18:28.885918284+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/files/loisonchambers-eurecat/cleanRL/lnmebn7s/file_stream\": net/http: request canceled (Client.Timeout exceeded while awaiting headers)"}
+{"time":"2026-04-21T16:19:03.016833655+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": dial tcp: lookup api.wandb.ai on 127.0.0.53:53: server misbehaving"}
+{"time":"2026-04-21T16:19:04.869738034+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/files/loisonchambers-eurecat/cleanRL/lnmebn7s/file_stream\": dial tcp: lookup api.wandb.ai on 127.0.0.53:53: server misbehaving"}
+{"time":"2026-04-21T16:20:03.020452593+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": dial tcp: lookup api.wandb.ai on 127.0.0.53:53: server misbehaving"}
+{"time":"2026-04-21T16:20:04.872678919+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/files/loisonchambers-eurecat/cleanRL/lnmebn7s/file_stream\": dial tcp: lookup api.wandb.ai on 127.0.0.53:53: server misbehaving"}
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+{"time":"2026-04-21T16:21:04.874796576+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/files/loisonchambers-eurecat/cleanRL/lnmebn7s/file_stream\": dial tcp: lookup api.wandb.ai on 127.0.0.53:53: server misbehaving"}
+{"time":"2026-04-21T16:22:03.026497993+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": dial tcp: lookup api.wandb.ai on 127.0.0.53:53: server misbehaving"}
+{"time":"2026-04-21T16:22:04.877342455+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/files/loisonchambers-eurecat/cleanRL/lnmebn7s/file_stream\": dial tcp: lookup api.wandb.ai on 127.0.0.53:53: server misbehaving"}
+{"time":"2026-04-21T16:23:03.029223469+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": dial tcp: lookup api.wandb.ai on 127.0.0.53:53: server misbehaving"}
+{"time":"2026-04-21T16:23:04.879837773+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/files/loisonchambers-eurecat/cleanRL/lnmebn7s/file_stream\": dial tcp: lookup api.wandb.ai on 127.0.0.53:53: server misbehaving"}
+{"time":"2026-04-21T16:24:03.032106758+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": dial tcp: lookup api.wandb.ai on 127.0.0.53:53: server misbehaving"}
+{"time":"2026-04-21T16:24:04.88343346+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/files/loisonchambers-eurecat/cleanRL/lnmebn7s/file_stream\": dial tcp: lookup api.wandb.ai on 127.0.0.53:53: server misbehaving"}
+{"time":"2026-04-21T16:25:03.034684975+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": dial tcp: lookup api.wandb.ai on 127.0.0.53:53: server misbehaving"}
+{"time":"2026-04-21T16:25:04.886253134+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/files/loisonchambers-eurecat/cleanRL/lnmebn7s/file_stream\": dial tcp: lookup api.wandb.ai on 127.0.0.53:53: server misbehaving"}
+{"time":"2026-04-21T16:26:03.037893385+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": dial tcp: lookup api.wandb.ai on 127.0.0.53:53: server misbehaving"}
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+{"time":"2026-04-21T16:27:03.040473678+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": dial tcp: lookup api.wandb.ai on 127.0.0.53:53: server misbehaving"}
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+{"time":"2026-04-21T16:29:04.897537612+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/files/loisonchambers-eurecat/cleanRL/lnmebn7s/file_stream\": dial tcp: lookup api.wandb.ai on 127.0.0.53:53: server misbehaving"}
+{"time":"2026-04-21T16:30:03.049632822+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": dial tcp: lookup api.wandb.ai on 127.0.0.53:53: server misbehaving"}
+{"time":"2026-04-21T16:30:04.900801494+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/files/loisonchambers-eurecat/cleanRL/lnmebn7s/file_stream\": dial tcp: lookup api.wandb.ai on 127.0.0.53:53: server misbehaving"}
+{"time":"2026-04-21T16:31:03.052559615+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/graphql\": dial tcp: lookup api.wandb.ai on 127.0.0.53:53: server misbehaving"}
+{"time":"2026-04-21T16:31:04.904615307+02:00","level":"INFO","msg":"api: retrying error","error":"Post \"https://api.wandb.ai/files/loisonchambers-eurecat/cleanRL/lnmebn7s/file_stream\": dial tcp: lookup api.wandb.ai on 127.0.0.53:53: server misbehaving"}
diff --git a/wandb/run-20260421_160121-lnmebn7s/logs/debug.log b/wandb/run-20260421_160121-lnmebn7s/logs/debug.log
new file mode 100644
index 0000000000000000000000000000000000000000..6cd1d99cda8a852af07d2baa50886a9d7a3739db
--- /dev/null
+++ b/wandb/run-20260421_160121-lnmebn7s/logs/debug.log
@@ -0,0 +1,120 @@
+2026-04-21 16:01:21,851 INFO    MainThread:211923 [wandb_setup.py:_flush():81] Current SDK version is 0.26.0
+2026-04-21 16:01:21,851 INFO    MainThread:211923 [wandb_setup.py:_flush():81] Configure stats pid to 211923
+2026-04-21 16:01:21,851 INFO    MainThread:211923 [wandb_setup.py:_flush():81] Loading settings from environment variables
+2026-04-21 16:01:21,851 INFO    MainThread:211923 [wandb_init.py:setup_run_log_directory():721] Logging user logs to /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/wandb/run-20260421_160121-lnmebn7s/logs/debug.log
+2026-04-21 16:01:21,851 INFO    MainThread:211923 [wandb_init.py:setup_run_log_directory():722] Logging internal logs to /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/wandb/run-20260421_160121-lnmebn7s/logs/debug-internal.log
+2026-04-21 16:01:21,911 INFO    MainThread:211923 [wandb_init.py:init():848] calling init triggers
+2026-04-21 16:01:21,911 INFO    MainThread:211923 [wandb_init.py:init():853] wandb.init called with sweep_config: {}
+config: {'exp_name': 'ppo', 'gym_id': 'CartPole-v1', 'learning_rate': 0.00025, 'seed': 1, 'total_timesteps': 25000, 'torch_deterministic': True, 'cuda': True, 'track': True, 'wandb_project_name': 'cleanRL', 'wandb_entity': None, 'num_envs': 4, 'capture_video': True, 'num_steps': 128, 'anneal_lr': True, 'gae': True, 'gamma': 0.99, 'gae_lambda': 0.95, 'num_minibatches': 4, 'update_epochs': 4, 'norm_adv': True, 'clip_coef': 0.2, 'clip_vloss': True, 'ent_coef': 0.01, 'vf_coef': 0.5, 'max_grad_norm': 0.5, 'target_kl': None, 'batch_size': 512, 'minibatch_size': 128, '_wandb': {'code_path': 'code/ppo.py'}}
+2026-04-21 16:01:21,911 INFO    MainThread:211923 [wandb_init.py:init():896] starting backend
+2026-04-21 16:01:22,186 INFO    MainThread:211923 [wandb_init.py:init():911] sending inform_init request
+2026-04-21 16:01:22,226 INFO    MainThread:211923 [wandb_init.py:init():919] backend started and connected
+2026-04-21 16:01:22,257 INFO    MainThread:211923 [wandb_init.py:init():989] updated telemetry
+2026-04-21 16:01:22,307 INFO    MainThread:211923 [wandb_init.py:init():1013] communicating run to backend with 90.0 second timeout
+2026-04-21 16:01:23,012 INFO    MainThread:211923 [wandb_init.py:init():1058] starting run threads in backend
+2026-04-21 16:01:23,125 INFO    MainThread:211923 [wandb_run.py:_console_start():2542] atexit reg
+2026-04-21 16:01:23,126 INFO    MainThread:211923 [wandb_run.py:_redirect():2391] redirect: wrap_raw
+2026-04-21 16:01:23,126 INFO    MainThread:211923 [wandb_run.py:_redirect():2460] Wrapping output streams.
+2026-04-21 16:01:23,126 INFO    MainThread:211923 [wandb_run.py:_redirect():2483] Redirects installed.
+2026-04-21 16:01:23,132 INFO    MainThread:211923 [wandb_init.py:init():1098] run started, returning control to user process
+2026-04-21 16:01:23,133 INFO    MainThread:211923 [wandb_run.py:_tensorboard_callback():1632] tensorboard callback: runs/CartPole-v1_ppo_1_1776780080, True
+2026-04-21 16:15:52,184 INFO    wandb-AsyncioManager-main:211923 [service_client.py:_forward_responses():134] Reached EOF.
+2026-04-21 16:15:52,185 INFO    wandb-AsyncioManager-main:211923 [mailbox.py:close():155] Closing mailbox, abandoning 1 handles.
+2026-04-21 16:31:19,827 ERROR   wandb-AsyncioManager-main:211923 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 45, in publish
+    await self._send_server_request(request)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 87, in _send_server_request
+    await self._drain_writer()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 104, in _drain_writer
+    await self._writer.drain()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 387, in drain
+    await self._protocol._drain_helper()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 190, in _drain_helper
+    raise ConnectionResetError('Connection lost')
+ConnectionResetError: Connection lost
+2026-04-21 16:31:19,833 ERROR   wandb-AsyncioManager-main:211923 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 45, in publish
+    await self._send_server_request(request)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 87, in _send_server_request
+    await self._drain_writer()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 104, in _drain_writer
+    await self._writer.drain()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 387, in drain
+    await self._protocol._drain_helper()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 190, in _drain_helper
+    raise ConnectionResetError('Connection lost')
+ConnectionResetError: Connection lost
+2026-04-21 16:31:19,834 ERROR   wandb-AsyncioManager-main:211923 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 45, in publish
+    await self._send_server_request(request)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 78, in _send_server_request
+    raise self._broken_exc.with_traceback(self._broken_tb)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 87, in _send_server_request
+    await self._drain_writer()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 104, in _drain_writer
+    await self._writer.drain()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 387, in drain
+    await self._protocol._drain_helper()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 190, in _drain_helper
+    raise ConnectionResetError('Connection lost')
+ConnectionResetError: Connection lost
+2026-04-21 16:31:19,834 ERROR   wandb-AsyncioManager-main:211923 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 45, in publish
+    await self._send_server_request(request)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 78, in _send_server_request
+    raise self._broken_exc.with_traceback(self._broken_tb)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 87, in _send_server_request
+    await self._drain_writer()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 104, in _drain_writer
+    await self._writer.drain()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 387, in drain
+    await self._protocol._drain_helper()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 190, in _drain_helper
+    raise ConnectionResetError('Connection lost')
+ConnectionResetError: Connection lost
+2026-04-21 16:31:19,834 ERROR   wandb-AsyncioManager-main:211923 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 45, in publish
+    await self._send_server_request(request)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 78, in _send_server_request
+    raise self._broken_exc.with_traceback(self._broken_tb)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 87, in _send_server_request
+    await self._drain_writer()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 104, in _drain_writer
+    await self._writer.drain()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 387, in drain
+    await self._protocol._drain_helper()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 190, in _drain_helper
+    raise ConnectionResetError('Connection lost')
+ConnectionResetError: Connection lost
+2026-04-21 16:31:19,835 ERROR   wandb-AsyncioManager-main:211923 [asyncio_manager.py:fn_wrap_exceptions():184] Uncaught exception in run_soon callback.
+Traceback (most recent call last):
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/asyncio_manager.py", line 182, in fn_wrap_exceptions
+    await fn()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 45, in publish
+    await self._send_server_request(request)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 78, in _send_server_request
+    raise self._broken_exc.with_traceback(self._broken_tb)
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 87, in _send_server_request
+    await self._drain_writer()
+  File "/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/wandb/sdk/lib/service/service_client.py", line 104, in _drain_writer
+    await self._writer.drain()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 387, in drain
+    await self._protocol._drain_helper()
+  File "/home/lois.fernandez@local.eurecat.org/.pyenv/versions/3.9.5/lib/python3.9/asyncio/streams.py", line 190, in _drain_helper
+    raise ConnectionResetError('Connection lost')
+ConnectionResetError: Connection lost
diff --git a/wandb/run-20260421_160121-lnmebn7s/run-lnmebn7s.wandb b/wandb/run-20260421_160121-lnmebn7s/run-lnmebn7s.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..fc026e41b63b86095195e25b252d853f3a68b163
--- /dev/null
+++ b/wandb/run-20260421_160121-lnmebn7s/run-lnmebn7s.wandb
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b9245a1fb1c40032584e7d799e2fc7fd3999b6c2c59f72b7362307e42754e35e
+size 262144
diff --git a/wandb/run-20260421_165557-ngbvlof1/files/code/ppo.py b/wandb/run-20260421_165557-ngbvlof1/files/code/ppo.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa186c92794fb5c701f689651e6ad772ed8c2a58
--- /dev/null
+++ b/wandb/run-20260421_165557-ngbvlof1/files/code/ppo.py
@@ -0,0 +1,313 @@
+import argparse
+import os
+from distutils.util import strtobool
+import time
+import random
+import numpy as np
+import gym
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.distributions.categorical import Categorical
+from torch.utils.tensorboard import SummaryWriter
+
+def make_env(gym_id, seed, idx, capture_video, run_name):
+    def thunk():
+        env = gym.make(gym_id)
+        env = gym.wrappers.RecordEpisodeStatistics(env)
+        if capture_video:
+            if idx == 0:
+                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", episode_trigger=lambda t: t % 1000 == 0)
+        env.seed(seed)
+        env.action_space.seed(seed)
+        env.observation_space.seed(seed)
+        return env
+    return thunk
+
+def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
+    torch.nn.init.orthogonal_(layer.weight, std)
+    torch.nn.init.constant_(layer.bias, bias_const)
+    return layer
+
+class Agent(nn.Module):
+    def __init__(self, envs):
+        super(Agent, self).__init__()
+        self.critic = nn.Sequential(
+            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,1), std=1.),
+        )
+
+        self.actor = nn.Sequential(
+            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
+        )
+
+    def get_value(self, x):
+        return self.critic(x)
+    
+    def get_action_and_value(self, x, action=None):
+        logits = self.actor(x)
+        probs = Categorical(logits=logits)
+        if action is None:
+            action = probs.sample()
+        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+        help='the name of this experiment')
+    parser.add_argument('--gym-id', type=str, default="CartPole-v1",
+        help='the id of the gym environment')
+    parser.add_argument('--learning-rate', type=float, default=2.5e-4,
+        help='the learning rate of the optimizer')
+    parser.add_argument('--seed', type=int, default=1,
+        help='seed of the experiment')
+    parser.add_argument('--total-timesteps', type=int, default=25000,
+        help='total timesteps of the experiments')
+    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+    parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help='if toggled, cuda will not be enabled by default')
+    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
+        help='if toggled, this experiment will be tracked with Weights and Biases')
+    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
+        help="the wandb's project name")
+    parser.add_argument('--wandb-entity', type=str, default=None,
+        help="the entity (team) of wandb's project")
+    parser.add_argument("--num-envs", type=int, default=4,
+    help="the number of parallel game environments")
+    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="whether to capture videos of the agent performances (check out `videos` folder)")
+    parser.add_argument("--num-steps", type=int, default=128,
+        help="the number of steps to run in each environment per policy rollout")
+    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggle learning rate annealing for policy and value networks")  
+    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Use GAE for advantage computation")  
+    parser.add_argument('--gamma', type=float, default=0.99,
+        help='the discount factor gamma')
+    parser.add_argument('--gae-lambda', type=float, default=0.95,
+        help='the lambda for the general advantage estimation')
+    parser.add_argument("--num-minibatches", type=int, default=4,
+        help="the number of mini-batches")
+    parser.add_argument("--update-epochs", type=int, default=4,
+        help="the K epochs to update the policy")
+    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggles advantages normalization")
+    parser.add_argument('--clip-coef', type=float, default=0.2,
+        help='the surrogate clipping coefficient')     
+    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
+    parser.add_argument('--ent-coef', type=float, default=0.01,
+        help='coefficient of the entropy')
+    parser.add_argument('--vf-coef', type=float, default=0.5,
+        help='coefficient of the value function')
+    parser.add_argument('--max-grad-norm', type=float, default=0.5,
+        help='the maximum norm for the gradient clipping')
+    parser.add_argument('--target-kl', type=float, default=None,
+        help='the target KL divergence threshold')
+    args = parser.parse_args()
+    args.batch_size = int(args.num_envs * args.num_steps)
+    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+    return args
+
+if __name__ == "__main__":
+    args = parse_args()
+    print(args)
+    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+    if args.track:
+        import wandb
+
+        wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            sync_tensorboard=True,
+            config=vars(args),
+            name=run_name,
+            monitor_gym=True,
+            save_code=True,
+        )
+    writer =SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
+    )
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
+
+    envs = gym.vector.SyncVectorEnv(
+        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
+    for i in range(args.num_envs)])
+    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
+
+    agent = Agent(envs).to(device)
+    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
+    
+    
+    # ALGO Logic: Storage setup
+    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
+    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
+    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
+
+    #TRY NOT TO MODIFY: start the game
+    global_step = 0
+    start_time =time.time()
+    next_obs = torch.Tensor(envs.reset()).to(device)
+    next_done = torch.zeros(args.num_envs).to(device)
+    num_updates = args.total_timesteps // args.batch_size
+
+    for update in range(1, num_updates+1):
+        # Annealing the rate if instructed to do so.abs
+        if args.anneal_lr:
+            frac = 1.0 -(update -1.0) / num_updates
+            lrnow = frac * args.learning_rate
+            optimizer.param_groups[0]["lr"]=lrnow
+        
+        for step in range(0, args.num_steps):
+            global_step += 1 * args.num_envs
+            obs[step] = next_obs
+            dones[step] = next_done
+
+            # ALGO LOGIC : action logic
+            with torch.no_grad():
+                action, logprob, _, value = agent.get_action_and_value(next_obs)
+                values[step] = value.flatten()
+            actions[step] = action
+            logprobs[step] = logprob
+
+            # TRY NOT TO MODIFY: execute the game and log data.abs
+            next_obs, reward, done, info = envs.step(action.cpu().numpy())
+            rewards[step] = torch.tensor(reward).to(device).view(-1)
+            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
+        
+            if isinstance(info, dict) and "episode" in info:
+                for item in info["episode"]:
+                    if item is not None:
+                        print(f"global_step={global_step}, episodic_return={item['r']}")
+                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
+                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
+                        break
+            
+            #bootstrap reward if not done
+            with torch.no_grad():
+                next_value = agent.get_value(next_obs).reshape(1,-1)
+                if args.gae:
+                    advantages = torch.zeros_like(rewards).to(device)
+                    lastgaelam = 0
+                    for t in reversed(range(args.num_steps)):
+                        if t == args.num_steps - 1:
+                            nextnonterminal = 1.0 - next_done
+                            nextvalues = next_value
+                        else:
+                            nextnonterminal = 1.0 - dones[t+1]
+                            nextvalues = values[t+1]
+                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
+                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
+                    returns =advantages + values
+                else:
+                    returns =torch.zeros_like(rewards).to(device)
+                    for t in reversed(range(args.num_steps)):
+                        if t== args.num_steps - 1:
+                            nextnonterminal = 1.0 -next_done
+                            next_return = next_value
+                        else:
+                            nextnonterminal = 1.0 - dones[t+1]
+                            next_return = returns[t+1]
+                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
+                    advantages = returns - values
+            
+            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
+            b_logprobs =logprobs.reshape(-1)
+            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
+            b_advantages =advantages.reshape(-1)
+            b_returns = returns.reshape(-1)
+            b_values = values.reshape(-1)
+
+            # Optimizaing the policy and value network
+            b_inds = np.arange(args.batch_size)
+            clipfracs = []
+            for epoch in range(args.update_epochs):
+                np.random.shuffle(b_inds)
+                for start in range(0,args.batch_size, args.minibatch_size):
+                    end = start + args.minibatch_size
+                    mb_inds = b_inds[start:end]
+
+                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
+                        b_obs[mb_inds], b_actions.long()[mb_inds]
+                    )
+                    logratio = newlogprob - b_logprobs[mb_inds]
+                    ratio = logratio.exp()
+
+                    with torch.no_grad():
+                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
+                        old_approx_kl = (-logratio).mean()
+                        approx_kl =((ratio-1) - logratio).mean()
+                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
+
+                    mb_advantages = b_advantages[mb_inds]
+                    if args.norm_adv:
+                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
+
+                    # Policy loss
+                    pg_loss1 = -mb_advantages * ratio
+                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
+                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
+
+                    # Value loss
+                    newvalue = newvalue.view(-1)
+                    if args.clip_vloss:
+                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
+                        v_clipped = b_values[mb_inds] + torch.clamp(
+                            newvalue - b_values[mb_inds],
+                            -args.clip_coef,
+                            args.clip_coef
+                        )
+                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
+                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
+                        v_loss = 0.5 * v_loss_max.mean()
+                    else:
+                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
+                    
+                    # Entropy loss
+                    entropy_loss = entropy.mean()
+                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
+
+                    optimizer.zero_grad()
+                    loss.backward()
+                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
+                    optimizer.step()
+            
+                if args.target_kl is not None:
+                    if approx_kl > args.target_kl:
+                        break
+
+        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
+        var_y = np.var(y_true)
+        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
+
+        # TRY NOT TO MODIFY: record rewards for plotting purposes
+        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
+        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
+        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
+        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
+        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
+        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
+        writer.add_scalar("losses/explained_variance",explained_var, global_step)
+        print("SPS:", int(global_step / (time.time() - start_time)))
+        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
+
+    envs.close()
+    writer.close()
\ No newline at end of file
diff --git a/wandb/run-20260421_165557-ngbvlof1/files/config.yaml b/wandb/run-20260421_165557-ngbvlof1/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..cd3cac0328ceb2c67aff24eb551b91ba4d9502bd
--- /dev/null
+++ b/wandb/run-20260421_165557-ngbvlof1/files/config.yaml
@@ -0,0 +1,103 @@
+_wandb:
+    value:
+        cli_version: 0.26.0
+        code_path: code/ppo.py
+        e:
+            uvt68ahs4pilrgadslzha5xkgk4qarkr:
+                args:
+                    - --track
+                    - --capture-video
+                codePath: ppo.py
+                codePathLocal: ppo.py
+                cpu_count: 12
+                cpu_count_logical: 14
+                disk:
+                    /:
+                        total: "499680116736"
+                        used: "141784096768"
+                email: loisfernandezcmp@gmail.com
+                executable: /home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/bin/python
+                git:
+                    commit: e1dc52c8934ce180fef18fbdf53a72281f134a52
+                    remote: https://github.com/loisonchambers/Unit-8-RL-course.git
+                host: BL-LFERNANDEZ.local.eurecat.org
+                memory:
+                    total: "33076023296"
+                os: Linux-6.17.0-20-generic-x86_64-with-glibc2.39
+                program: /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8/ppo.py
+                python: CPython 3.9.5
+                root: /home/lois.fernandez@local.eurecat.org/Documents/Manuales/RL Course Huggingface/Unit 8
+                startedAt: "2026-04-21T14:55:57.783877Z"
+                writerId: uvt68ahs4pilrgadslzha5xkgk4qarkr
+        m: []
+        python_version: 3.9.5
+        t:
+            "1":
+                - 1
+            "2":
+                - 1
+            "3":
+                - 13
+                - 16
+                - 35
+                - 41
+            "4": 3.9.5
+            "5": 0.26.0
+            "12": 0.26.0
+            "13": linux-x86_64
+anneal_lr:
+    value: true
+batch_size:
+    value: 512
+capture_video:
+    value: true
+clip_coef:
+    value: 0.2
+clip_vloss:
+    value: true
+cuda:
+    value: true
+ent_coef:
+    value: 0.01
+exp_name:
+    value: ppo
+gae:
+    value: true
+gae_lambda:
+    value: 0.95
+gamma:
+    value: 0.99
+gym_id:
+    value: CartPole-v1
+learning_rate:
+    value: 0.00025
+max_grad_norm:
+    value: 0.5
+minibatch_size:
+    value: 128
+norm_adv:
+    value: true
+num_envs:
+    value: 4
+num_minibatches:
+    value: 4
+num_steps:
+    value: 128
+seed:
+    value: 1
+target_kl:
+    value: null
+torch_deterministic:
+    value: true
+total_timesteps:
+    value: 25000
+track:
+    value: true
+update_epochs:
+    value: 4
+vf_coef:
+    value: 0.5
+wandb_entity:
+    value: null
+wandb_project_name:
+    value: cleanRL
diff --git a/wandb/run-20260421_165557-ngbvlof1/files/diff.patch b/wandb/run-20260421_165557-ngbvlof1/files/diff.patch
new file mode 100644
index 0000000000000000000000000000000000000000..7eac201403b7a518f382a7af485673ef065e473a
--- /dev/null
+++ b/wandb/run-20260421_165557-ngbvlof1/files/diff.patch
@@ -0,0 +1,1316 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..bc1e1ab 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
++    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"},
++    {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"},
++]
++
++[[package]]
++name = "charset-normalizer"
++version = "3.4.7"
++description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
++optional = false
++python-versions = ">=3.7"
++files = [
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++
++[[package]]
++name = "click"
++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
++smmap = ">=3.0.1,<6"
++
++[[package]]
++name = "gitpython"
++version = "3.1.46"
++description = "GitPython is a Python library used to interact with Git repositories"
++optional = false
++python-versions = ">=3.7"
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+ xmp = ["defusedxml"]
+ 
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++    {file = "pyyaml-6.0.3-cp39-cp39-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5ed875a24292240029e4483f9d4a4b8a1ae08843b9c54f43fcc11e404532a8a5"},
++    {file = "pyyaml-6.0.3-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0150219816b6a1fa26fb4699fb7daa9caf09eb1999f3b70fb6e786805e80375a"},
++    {file = "pyyaml-6.0.3-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:fa160448684b4e94d80416c0fa4aac48967a969efe22931448d853ada8baf926"},
++    {file = "pyyaml-6.0.3-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:27c0abcb4a5dac13684a37f76e701e054692a9b2d3064b70f5e4eb54810553d7"},
++    {file = "pyyaml-6.0.3-cp39-cp39-win32.whl", hash = "sha256:1ebe39cb5fc479422b83de611d14e2c0d3bb2a18bbcb01f229ab3cfbd8fee7a0"},
++    {file = "pyyaml-6.0.3-cp39-cp39-win_amd64.whl", hash = "sha256:2e71d11abed7344e42a8849600193d15b6def118602c4c176f748e4583246007"},
++    {file = "pyyaml-6.0.3.tar.gz", hash = "sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f"},
++]
++
++[[package]]
++name = "requests"
++version = "2.32.5"
++description = "Python HTTP for Humans."
+ optional = false
+ python-versions = ">=3.9"
+ files = [
+-    {file = "setuptools-82.0.1-py3-none-any.whl", hash = "sha256:a59e362652f08dcd477c78bb6e7bd9d80a7995bc73ce773050228a348ce2e5bb"},
+-    {file = "setuptools-82.0.1.tar.gz", hash = "sha256:7d872682c5d01cfde07da7bccc7b65469d3dca203318515ada1de5eda35efbf9"},
++    {file = "requests-2.32.5-py3-none-any.whl", hash = "sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6"},
++    {file = "requests-2.32.5.tar.gz", hash = "sha256:dbba0bac56e100853db0ea71b82b4dfd5fe2bf6d3754a8893c3af500cec7d7cf"},
+ ]
+ 
++[package.dependencies]
++certifi = ">=2017.4.17"
++charset_normalizer = ">=2,<4"
++idna = ">=2.5,<4"
++urllib3 = ">=1.21.1,<3"
++
+ [package.extras]
+-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.13.0)"]
+-core = ["importlib_metadata (>=6)", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more_itertools", "more_itertools (>=8.8)", "packaging (>=24.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
+-cover = ["pytest-cov"]
+-doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier", "towncrier (<24.7)"]
+-enabler = ["pytest-enabler (>=2.2)"]
+-test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.7.2)", "jaraco.test (>=5.5)", "packaging (>=24.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-home (>=0.5)", "pytest-perf", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel (>=0.44.0)"]
+-type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.18.*)", "pytest-mypy"]
++socks = ["PySocks (>=1.5.6,!=1.5.7)"]
++use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
++
++[[package]]
++name = "sentry-sdk"
++version = "2.58.0"
++description = "Python client for Sentry (https://sentry.io)"
++optional = false
++python-versions = ">=3.6"
++files = [
++    {file = "sentry_sdk-2.58.0-py2.py3-none-any.whl", hash = "sha256:688d1c704ddecf382ea3326f21a67453d4caa95592d722b7c780a36a9d23109e"},
++    {file = "sentry_sdk-2.58.0.tar.gz", hash = "sha256:c1144d947352d54e5b7daa63596d9f848adf684989c06c4f5a659f0c85a18f6f"},
++]
++
++[package.dependencies]
++certifi = "*"
++urllib3 = ">=1.26.11"
++
++[package.extras]
++aiohttp = ["aiohttp (>=3.5)"]
++anthropic = ["anthropic (>=0.16)"]
++arq = ["arq (>=0.23)"]
++asyncio = ["httpcore[asyncio] (==1.*)"]
++asyncpg = ["asyncpg (>=0.23)"]
++beam = ["apache-beam (>=2.12)"]
++bottle = ["bottle (>=0.12.13)"]
++celery = ["celery (>=3)"]
++celery-redbeat = ["celery-redbeat (>=2)"]
++chalice = ["chalice (>=1.16.0)"]
++clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
++django = ["django (>=1.8)"]
++falcon = ["falcon (>=1.4)"]
++fastapi = ["fastapi (>=0.79.0)"]
++flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
++google-genai = ["google-genai (>=1.29.0)"]
++grpcio = ["grpcio (>=1.21.1)", "protobuf (>=3.8.0)"]
++http2 = ["httpcore[http2] (==1.*)"]
++httpx = ["httpx (>=0.16.0)"]
++huey = ["huey (>=2)"]
++huggingface-hub = ["huggingface_hub (>=0.22)"]
++langchain = ["langchain (>=0.0.210)"]
++langgraph = ["langgraph (>=0.6.6)"]
++launchdarkly = ["launchdarkly-server-sdk (>=9.8.0)"]
++litellm = ["litellm (>=1.77.5,!=1.82.7,!=1.82.8)"]
++litestar = ["litestar (>=2.0.0)"]
++loguru = ["loguru (>=0.5)"]
++mcp = ["mcp (>=1.15.0)"]
++openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
++openfeature = ["openfeature-sdk (>=0.7.1)"]
++opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
++opentelemetry-experimental = ["opentelemetry-distro"]
++opentelemetry-otlp = ["opentelemetry-distro[otlp] (>=0.35b0)"]
++pure-eval = ["asttokens", "executing", "pure_eval"]
++pydantic-ai = ["pydantic-ai (>=1.0.0)"]
++pymongo = ["pymongo (>=3.1)"]
++pyspark = ["pyspark (>=2.4.4)"]
++quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
++rq = ["rq (>=0.6)"]
++sanic = ["sanic (>=0.8)"]
++sqlalchemy = ["sqlalchemy (>=1.2)"]
++starlette = ["starlette (>=0.19.1)"]
++starlite = ["starlite (>=1.48)"]
++statsig = ["statsig (>=0.55.3)"]
++tornado = ["tornado (>=6)"]
++unleash = ["UnleashClient (>=6.0.1)"]
++
++[[package]]
++name = "setuptools"
++version = "65.5.0"
++description = "Easily download, build, install, upgrade, and uninstall Python packages"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "setuptools-65.5.0-py3-none-any.whl", hash = "sha256:f62ea9da9ed6289bfe868cd6845968a2c854d1427f8548d52cae02a42b4f0356"},
++    {file = "setuptools-65.5.0.tar.gz", hash = "sha256:512e5536220e38146176efb833d4a62aa726b7bbff82cfbc8ba9eaa3996e0b17"},
++]
++
++[package.extras]
++docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
++testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
++testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
++
++[[package]]
++name = "smmap"
++version = "5.0.3"
++description = "A pure Python implementation of a sliding window memory map manager"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "smmap-5.0.3-py3-none-any.whl", hash = "sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f"},
++    {file = "smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c"},
++]
+ 
+ [[package]]
+ name = "sympy"
+@@ -1197,6 +1881,81 @@ files = [
+     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+ ]
+ 
++[[package]]
++name = "typing-inspection"
++version = "0.4.2"
++description = "Runtime typing introspection tools"
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
++    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
++]
++
++[package.dependencies]
++typing-extensions = ">=4.12.0"
++
++[[package]]
++name = "urllib3"
++version = "2.6.3"
++description = "HTTP library with thread-safe connection pooling, file post, and more."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
++brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"]
++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:1ece94a2a5eda1d0e3a2d8a2fd28aa0187705d6efa5ac4c0b8680083583b7ec1"},
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:92f6f303fe2af50e3f711833a835150f9b4d8082874bfd9868cf15491ea2947e"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:a17aae051a31831388cff880251c1b5bc38fbf6a283a0ee7c543709e8e9633d1"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:28a14ffc014e523498d077dfde12839b7be586ca8c3190e72e7167c1aea6ee4c"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1995,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "f11507e147d7c2cb9dd240bbb479024772dae6eaca788addca05b86fe9a8479c"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..aa186c9 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -2,8 +2,62 @@ import argparse
+ import os
+ from distutils.util import strtobool
+ import time
++import random
++import numpy as np
++import gym
++import torch
++import torch.nn as nn
++import torch.optim as optim
++from torch.distributions.categorical import Categorical
+ from torch.utils.tensorboard import SummaryWriter
+ 
++def make_env(gym_id, seed, idx, capture_video, run_name):
++    def thunk():
++        env = gym.make(gym_id)
++        env = gym.wrappers.RecordEpisodeStatistics(env)
++        if capture_video:
++            if idx == 0:
++                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", episode_trigger=lambda t: t % 1000 == 0)
++        env.seed(seed)
++        env.action_space.seed(seed)
++        env.observation_space.seed(seed)
++        return env
++    return thunk
++
++def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
++    torch.nn.init.orthogonal_(layer.weight, std)
++    torch.nn.init.constant_(layer.bias, bias_const)
++    return layer
++
++class Agent(nn.Module):
++    def __init__(self, envs):
++        super(Agent, self).__init__()
++        self.critic = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,1), std=1.),
++        )
++
++        self.actor = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
++        )
++
++    def get_value(self, x):
++        return self.critic(x)
++    
++    def get_action_and_value(self, x, action=None):
++        logits = self.actor(x)
++        probs = Categorical(logits=logits)
++        if action is None:
++            action = probs.sample()
++        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
++
+ def parse_args():
+     parser = argparse.ArgumentParser()
+     parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+@@ -16,21 +70,244 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
++    parser.add_argument("--num-envs", type=int, default=4,
++    help="the number of parallel game environments")
++    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
++        help="whether to capture videos of the agent performances (check out `videos` folder)")
++    parser.add_argument("--num-steps", type=int, default=128,
++        help="the number of steps to run in each environment per policy rollout")
++    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggle learning rate annealing for policy and value networks")  
++    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Use GAE for advantage computation")  
++    parser.add_argument('--gamma', type=float, default=0.99,
++        help='the discount factor gamma')
++    parser.add_argument('--gae-lambda', type=float, default=0.95,
++        help='the lambda for the general advantage estimation')
++    parser.add_argument("--num-minibatches", type=int, default=4,
++        help="the number of mini-batches")
++    parser.add_argument("--update-epochs", type=int, default=4,
++        help="the K epochs to update the policy")
++    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles advantages normalization")
++    parser.add_argument('--clip-coef', type=float, default=0.2,
++        help='the surrogate clipping coefficient')     
++    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
++    parser.add_argument('--ent-coef', type=float, default=0.01,
++        help='coefficient of the entropy')
++    parser.add_argument('--vf-coef', type=float, default=0.5,
++        help='coefficient of the value function')
++    parser.add_argument('--max-grad-norm', type=float, default=0.5,
++        help='the maximum norm for the gradient clipping')
++    parser.add_argument('--target-kl', type=float, default=None,
++        help='the target KL divergence threshold')
+     args = parser.parse_args()
++    args.batch_size = int(args.num_envs * args.num_steps)
++    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    envs = gym.vector.SyncVectorEnv(
++        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
++    for i in range(args.num_envs)])
++    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
++
++    agent = Agent(envs).to(device)
++    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
++    
++    
++    # ALGO Logic: Storage setup
++    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
++    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
++    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
++
++    #TRY NOT TO MODIFY: start the game
++    global_step = 0
++    start_time =time.time()
++    next_obs = torch.Tensor(envs.reset()).to(device)
++    next_done = torch.zeros(args.num_envs).to(device)
++    num_updates = args.total_timesteps // args.batch_size
++
++    for update in range(1, num_updates+1):
++        # Annealing the rate if instructed to do so.abs
++        if args.anneal_lr:
++            frac = 1.0 -(update -1.0) / num_updates
++            lrnow = frac * args.learning_rate
++            optimizer.param_groups[0]["lr"]=lrnow
++        
++        for step in range(0, args.num_steps):
++            global_step += 1 * args.num_envs
++            obs[step] = next_obs
++            dones[step] = next_done
++
++            # ALGO LOGIC : action logic
++            with torch.no_grad():
++                action, logprob, _, value = agent.get_action_and_value(next_obs)
++                values[step] = value.flatten()
++            actions[step] = action
++            logprobs[step] = logprob
++
++            # TRY NOT TO MODIFY: execute the game and log data.abs
++            next_obs, reward, done, info = envs.step(action.cpu().numpy())
++            rewards[step] = torch.tensor(reward).to(device).view(-1)
++            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
++        
++            if isinstance(info, dict) and "episode" in info:
++                for item in info["episode"]:
++                    if item is not None:
++                        print(f"global_step={global_step}, episodic_return={item['r']}")
++                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
++                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
++                        break
++            
++            #bootstrap reward if not done
++            with torch.no_grad():
++                next_value = agent.get_value(next_obs).reshape(1,-1)
++                if args.gae:
++                    advantages = torch.zeros_like(rewards).to(device)
++                    lastgaelam = 0
++                    for t in reversed(range(args.num_steps)):
++                        if t == args.num_steps - 1:
++                            nextnonterminal = 1.0 - next_done
++                            nextvalues = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            nextvalues = values[t+1]
++                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
++                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
++                    returns =advantages + values
++                else:
++                    returns =torch.zeros_like(rewards).to(device)
++                    for t in reversed(range(args.num_steps)):
++                        if t== args.num_steps - 1:
++                            nextnonterminal = 1.0 -next_done
++                            next_return = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            next_return = returns[t+1]
++                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
++                    advantages = returns - values
++            
++            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
++            b_logprobs =logprobs.reshape(-1)
++            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
++            b_advantages =advantages.reshape(-1)
++            b_returns = returns.reshape(-1)
++            b_values = values.reshape(-1)
++
++            # Optimizaing the policy and value network
++            b_inds = np.arange(args.batch_size)
++            clipfracs = []
++            for epoch in range(args.update_epochs):
++                np.random.shuffle(b_inds)
++                for start in range(0,args.batch_size, args.minibatch_size):
++                    end = start + args.minibatch_size
++                    mb_inds = b_inds[start:end]
++
++                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
++                        b_obs[mb_inds], b_actions.long()[mb_inds]
++                    )
++                    logratio = newlogprob - b_logprobs[mb_inds]
++                    ratio = logratio.exp()
++
++                    with torch.no_grad():
++                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
++                        old_approx_kl = (-logratio).mean()
++                        approx_kl =((ratio-1) - logratio).mean()
++                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
++
++                    mb_advantages = b_advantages[mb_inds]
++                    if args.norm_adv:
++                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
++
++                    # Policy loss
++                    pg_loss1 = -mb_advantages * ratio
++                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
++                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
++
++                    # Value loss
++                    newvalue = newvalue.view(-1)
++                    if args.clip_vloss:
++                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
++                        v_clipped = b_values[mb_inds] + torch.clamp(
++                            newvalue - b_values[mb_inds],
++                            -args.clip_coef,
++                            args.clip_coef
++                        )
++                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
++                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
++                        v_loss = 0.5 * v_loss_max.mean()
++                    else:
++                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
++                    
++                    # Entropy loss
++                    entropy_loss = entropy.mean()
++                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
++
++                    optimizer.zero_grad()
++                    loss.backward()
++                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
++                    optimizer.step()
++            
++                if args.target_kl is not None:
++                    if approx_kl > args.target_kl:
++                        break
++
++        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
++        var_y = np.var(y_true)
++        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
++
++        # TRY NOT TO MODIFY: record rewards for plotting purposes
++        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
++        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
++        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
++        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
++        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
++        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
++        writer.add_scalar("losses/explained_variance",explained_var, global_step)
++        print("SPS:", int(global_step / (time.time() - start_time)))
++        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
++
++    envs.close()
++    writer.close()
+\ No newline at end of file
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..d2ae71c 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.25.1"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_165557-ngbvlof1/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch b/wandb/run-20260421_165557-ngbvlof1/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
new file mode 100644
index 0000000000000000000000000000000000000000..7eac201403b7a518f382a7af485673ef065e473a
--- /dev/null
+++ b/wandb/run-20260421_165557-ngbvlof1/files/diff_e1dc52c8934ce180fef18fbdf53a72281f134a52.patch
@@ -0,0 +1,1316 @@
+diff --git a/poetry.lock b/poetry.lock
+index f147a0d..bc1e1ab 100644
+--- a/poetry.lock
++++ b/poetry.lock
+@@ -11,6 +11,180 @@ files = [
+     {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+ ]
+ 
++[[package]]
++name = "annotated-types"
++version = "0.7.0"
++description = "Reusable constraint types to use with typing.Annotated"
++optional = false
++python-versions = ">=3.8"
++files = [
++    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
++    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
++]
++
++[[package]]
++name = "certifi"
++version = "2026.2.25"
++description = "Python package for providing Mozilla's CA Bundle."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "certifi-2026.2.25-py3-none-any.whl", hash = "sha256:027692e4402ad994f1c42e52a4997a9763c646b73e4096e4d5d6db8af1d6f0fa"},
++    {file = "certifi-2026.2.25.tar.gz", hash = "sha256:e887ab5cee78ea814d3472169153c2d12cd43b14bd03329a39a9c6e2e80bfba7"},
++]
++
++[[package]]
++name = "charset-normalizer"
++version = "3.4.7"
++description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "charset_normalizer-3.4.7-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:cdd68a1fb318e290a2077696b7eb7a21a49163c455979c639bf5a5dcdc46617d"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e17b8d5d6a8c47c85e68ca8379def1303fd360c3e22093a807cd34a71cd082b8"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:511ef87c8aec0783e08ac18565a16d435372bc1ac25a91e6ac7f5ef2b0bff790"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:007d05ec7321d12a40227aae9e2bc6dca73f3cb21058999a1df9e193555a9dcc"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:cf29836da5119f3c8a8a70667b0ef5fdca3bb12f80fd06487cfa575b3909b393"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux_2_31_armv7l.whl", hash = "sha256:12d8baf840cc7889b37c7c770f478adea7adce3dcb3944d02ec87508e2dcf153"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:d560742f3c0d62afaccf9f41fe485ed69bd7661a241f86a3ef0f0fb8b1a397af"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:b14b2d9dac08e28bb8046a1a0434b1750eb221c8f5b87a68f4fa11a6f97b5e34"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_armv7l.whl", hash = "sha256:bc17a677b21b3502a21f66a8cc64f5bfad4df8a0b8434d661666f8ce90ac3af1"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:750e02e074872a3fad7f233b47734166440af3cdea0add3e95163110816d6752"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_riscv64.whl", hash = "sha256:4e5163c14bffd570ef2affbfdd77bba66383890797df43dc8b4cc7d6f500bf53"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:6ed74185b2db44f41ef35fd1617c5888e59792da9bbc9190d6c7300617182616"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:94e1885b270625a9a828c9793b4d52a64445299baa1fea5a173bf1d3dd9a1a5a"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-win32.whl", hash = "sha256:6785f414ae0f3c733c437e0f3929197934f526d19dfaa75e18fdb4f94c6fb374"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-win_amd64.whl", hash = "sha256:6696b7688f54f5af4462118f0bfa7c1621eeb87154f77fa04b9295ce7a8f2943"},
++    {file = "charset_normalizer-3.4.7-cp310-cp310-win_arm64.whl", hash = "sha256:66671f93accb62ed07da56613636f3641f1a12c13046ce91ffc923721f23c008"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:7641bb8895e77f921102f72833904dcd9901df5d6d72a2ab8f31d04b7e51e4e7"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:202389074300232baeb53ae2569a60901f7efadd4245cf3a3bf0617d60b439d7"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:30b8d1d8c52a48c2c5690e152c169b673487a2a58de1ec7393196753063fcd5e"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:532bc9bf33a68613fd7d65e4b1c71a6a38d7d42604ecf239c77392e9b4e8998c"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2fe249cb4651fd12605b7288b24751d8bfd46d35f12a20b1ba33dea122e690df"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-manylinux_2_31_armv7l.whl", hash = "sha256:65bcd23054beab4d166035cabbc868a09c1a49d1efe458fe8e4361215df40265"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:08e721811161356f97b4059a9ba7bafb23ea5ee2255402c42881c214e173c6b4"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:e060d01aec0a910bdccb8be71faf34e7799ce36950f8294c8bf612cba65a2c9e"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-musllinux_1_2_armv7l.whl", hash = "sha256:38c0109396c4cfc574d502df99742a45c72c08eff0a36158b6f04000043dbf38"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:1c2a768fdd44ee4a9339a9b0b130049139b8ce3c01d2ce09f67f5a68048d477c"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:1a87ca9d5df6fe460483d9a5bbf2b18f620cbed41b432e2bddb686228282d10b"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:d635aab80466bc95771bb78d5370e74d36d1fe31467b6b29b8b57b2a3cd7d22c"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:ae196f021b5e7c78e918242d217db021ed2a6ace2bc6ae94c0fc596221c7f58d"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-win32.whl", hash = "sha256:adb2597b428735679446b46c8badf467b4ca5f5056aae4d51a19f9570301b1ad"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-win_amd64.whl", hash = "sha256:8e385e4267ab76874ae30db04c627faaaf0b509e1ccc11a95b3fc3e83f855c00"},
++    {file = "charset_normalizer-3.4.7-cp311-cp311-win_arm64.whl", hash = "sha256:d4a48e5b3c2a489fae013b7589308a40146ee081f6f509e047e0e096084ceca1"},
++    {file = "charset_normalizer-3.4.7-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:eca9705049ad3c7345d574e3510665cb2cf844c2f2dcfe675332677f081cbd46"},
++    {file = "charset_normalizer-3.4.7-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6178f72c5508bfc5fd446a5905e698c6212932f25bcdd4b47a757a50605a90e2"},
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++]
++
++[[package]]
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++version = "8.1.8"
++description = "Composable command line interface toolkit"
++optional = false
++python-versions = ">=3.7"
++files = [
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++    {file = "click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a"},
++]
++
++[package.dependencies]
++colorama = {version = "*", markers = "platform_system == \"Windows\""}
++
+ [[package]]
+ name = "cloudpickle"
+ version = "3.1.2"
+@@ -149,6 +323,20 @@ files = [
+ [package.extras]
+ toml = ["tomli"]
+ 
++[[package]]
++name = "eval-type-backport"
++version = "0.3.1"
++description = "Like `typing._eval_type`, but lets older Python versions use newer typing features."
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "eval_type_backport-0.3.1-py3-none-any.whl", hash = "sha256:279ab641905e9f11129f56a8a78f493518515b83402b860f6f06dd7c011fdfa8"},
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++]
++
++[package.extras]
++tests = ["pytest"]
++
+ [[package]]
+ name = "exceptiongroup"
+ version = "1.3.1"
+@@ -227,6 +415,39 @@ test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto
+ test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+ tqdm = ["tqdm"]
+ 
++[[package]]
++name = "gitdb"
++version = "4.0.12"
++description = "Git Object Database"
++optional = false
++python-versions = ">=3.7"
++files = [
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++]
++
++[package.dependencies]
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++
++[[package]]
++name = "gitpython"
++version = "3.1.46"
++description = "GitPython is a Python library used to interact with Git repositories"
++optional = false
++python-versions = ">=3.7"
++files = [
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++
++[package.dependencies]
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++typing-extensions = {version = ">=3.10.0.2", markers = "python_version < \"3.10\""}
++
++[package.extras]
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++test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy (==1.18.2)", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
++
+ [[package]]
+ name = "grpcio"
+ version = "1.80.0"
+@@ -303,6 +524,45 @@ typing-extensions = ">=4.12,<5.0"
+ [package.extras]
+ protobuf = ["grpcio-tools (>=1.80.0)"]
+ 
++[[package]]
++name = "gym"
++version = "0.25.1"
++description = "Gym: A universal API for reinforcement learning environments"
++optional = false
++python-versions = ">=3.6"
++files = [
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++
++[package.dependencies]
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++importlib_metadata = {version = ">=4.8.0", markers = "python_version < \"3.10\""}
++numpy = ">=1.18.0"
++
++[package.extras]
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++toy-text = ["pygame (==2.1.0)"]
++
++[[package]]
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++version = "0.1.0"
++description = "Notices for gym"
++optional = false
++python-versions = "*"
++files = [
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++]
++
+ [[package]]
+ name = "gymnasium"
+ version = "0.29.1"
+@@ -334,6 +594,36 @@ other = ["lz4 (>=3.1.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-pyt
+ testing = ["pytest (==7.1.3)", "scipy (>=1.7.3)"]
+ toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+ 
++[[package]]
++name = "idna"
++version = "3.11"
++description = "Internationalized Domain Names in Applications (IDNA)"
++optional = false
++python-versions = ">=3.8"
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++[[package]]
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+ [[package]]
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+ 
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++
++[[package]]
++name = "requests"
++version = "2.32.5"
++description = "Python HTTP for Humans."
+ optional = false
+ python-versions = ">=3.9"
+ files = [
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+ ]
+ 
++[package.dependencies]
++certifi = ">=2017.4.17"
++charset_normalizer = ">=2,<4"
++idna = ">=2.5,<4"
++urllib3 = ">=1.21.1,<3"
++
+ [package.extras]
+-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)", "ruff (>=0.13.0)"]
+-core = ["importlib_metadata (>=6)", "jaraco.functools (>=4)", "jaraco.text (>=3.7)", "more_itertools", "more_itertools (>=8.8)", "packaging (>=24.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"]
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+-enabler = ["pytest-enabler (>=2.2)"]
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+-type = ["importlib_metadata (>=7.0.2)", "jaraco.develop (>=7.21)", "mypy (==1.18.*)", "pytest-mypy"]
++socks = ["PySocks (>=1.5.6,!=1.5.7)"]
++use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
++
++[[package]]
++name = "sentry-sdk"
++version = "2.58.0"
++description = "Python client for Sentry (https://sentry.io)"
++optional = false
++python-versions = ">=3.6"
++files = [
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++]
++
++[package.dependencies]
++certifi = "*"
++urllib3 = ">=1.26.11"
++
++[package.extras]
++aiohttp = ["aiohttp (>=3.5)"]
++anthropic = ["anthropic (>=0.16)"]
++arq = ["arq (>=0.23)"]
++asyncio = ["httpcore[asyncio] (==1.*)"]
++asyncpg = ["asyncpg (>=0.23)"]
++beam = ["apache-beam (>=2.12)"]
++bottle = ["bottle (>=0.12.13)"]
++celery = ["celery (>=3)"]
++celery-redbeat = ["celery-redbeat (>=2)"]
++chalice = ["chalice (>=1.16.0)"]
++clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
++django = ["django (>=1.8)"]
++falcon = ["falcon (>=1.4)"]
++fastapi = ["fastapi (>=0.79.0)"]
++flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
++google-genai = ["google-genai (>=1.29.0)"]
++grpcio = ["grpcio (>=1.21.1)", "protobuf (>=3.8.0)"]
++http2 = ["httpcore[http2] (==1.*)"]
++httpx = ["httpx (>=0.16.0)"]
++huey = ["huey (>=2)"]
++huggingface-hub = ["huggingface_hub (>=0.22)"]
++langchain = ["langchain (>=0.0.210)"]
++langgraph = ["langgraph (>=0.6.6)"]
++launchdarkly = ["launchdarkly-server-sdk (>=9.8.0)"]
++litellm = ["litellm (>=1.77.5,!=1.82.7,!=1.82.8)"]
++litestar = ["litestar (>=2.0.0)"]
++loguru = ["loguru (>=0.5)"]
++mcp = ["mcp (>=1.15.0)"]
++openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
++openfeature = ["openfeature-sdk (>=0.7.1)"]
++opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
++opentelemetry-experimental = ["opentelemetry-distro"]
++opentelemetry-otlp = ["opentelemetry-distro[otlp] (>=0.35b0)"]
++pure-eval = ["asttokens", "executing", "pure_eval"]
++pydantic-ai = ["pydantic-ai (>=1.0.0)"]
++pymongo = ["pymongo (>=3.1)"]
++pyspark = ["pyspark (>=2.4.4)"]
++quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
++rq = ["rq (>=0.6)"]
++sanic = ["sanic (>=0.8)"]
++sqlalchemy = ["sqlalchemy (>=1.2)"]
++starlette = ["starlette (>=0.19.1)"]
++starlite = ["starlite (>=1.48)"]
++statsig = ["statsig (>=0.55.3)"]
++tornado = ["tornado (>=6)"]
++unleash = ["UnleashClient (>=6.0.1)"]
++
++[[package]]
++name = "setuptools"
++version = "65.5.0"
++description = "Easily download, build, install, upgrade, and uninstall Python packages"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "setuptools-65.5.0-py3-none-any.whl", hash = "sha256:f62ea9da9ed6289bfe868cd6845968a2c854d1427f8548d52cae02a42b4f0356"},
++    {file = "setuptools-65.5.0.tar.gz", hash = "sha256:512e5536220e38146176efb833d4a62aa726b7bbff82cfbc8ba9eaa3996e0b17"},
++]
++
++[package.extras]
++docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
++testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
++testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
++
++[[package]]
++name = "smmap"
++version = "5.0.3"
++description = "A pure Python implementation of a sliding window memory map manager"
++optional = false
++python-versions = ">=3.7"
++files = [
++    {file = "smmap-5.0.3-py3-none-any.whl", hash = "sha256:c106e05d5a61449cf6ba9a1e650227ecfb141590d2a98412103ff35d89fc7b2f"},
++    {file = "smmap-5.0.3.tar.gz", hash = "sha256:4d9debb8b99007ae47165abc08670bd74cb74b5227dda7f643eccc4e9eb5642c"},
++]
+ 
+ [[package]]
+ name = "sympy"
+@@ -1197,6 +1881,81 @@ files = [
+     {file = "typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466"},
+ ]
+ 
++[[package]]
++name = "typing-inspection"
++version = "0.4.2"
++description = "Runtime typing introspection tools"
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
++    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
++]
++
++[package.dependencies]
++typing-extensions = ">=4.12.0"
++
++[[package]]
++name = "urllib3"
++version = "2.6.3"
++description = "HTTP library with thread-safe connection pooling, file post, and more."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"},
++    {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"},
++]
++
++[package.extras]
++brotli = ["brotli (>=1.2.0)", "brotlicffi (>=1.2.0.0)"]
++h2 = ["h2 (>=4,<5)"]
++socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
++zstd = ["backports-zstd (>=1.0.0)"]
++
++[[package]]
++name = "wandb"
++version = "0.26.0"
++description = "A CLI and library for interacting with the Weights & Biases API."
++optional = false
++python-versions = ">=3.9"
++files = [
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:1ece94a2a5eda1d0e3a2d8a2fd28aa0187705d6efa5ac4c0b8680083583b7ec1"},
++    {file = "wandb-0.26.0-py3-none-macosx_12_0_x86_64.whl", hash = "sha256:92f6f303fe2af50e3f711833a835150f9b4d8082874bfd9868cf15491ea2947e"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:a17aae051a31831388cff880251c1b5bc38fbf6a283a0ee7c543709e8e9633d1"},
++    {file = "wandb-0.26.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:28a14ffc014e523498d077dfde12839b7be586ca8c3190e72e7167c1aea6ee4c"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:fb9a63babeee044fecf65a4675f7dfb0efaea4986e498a3bc8f948558af877e7"},
++    {file = "wandb-0.26.0-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:3eb88fb556a64bf4492cf571bb851d47871901c096f0540d841ccb50f5cbeb66"},
++    {file = "wandb-0.26.0-py3-none-win32.whl", hash = "sha256:362828d48d21dd4877e28fdce40421ebdfc16d1fe0b59e8371b12d75bbc3f1e7"},
++    {file = "wandb-0.26.0-py3-none-win_amd64.whl", hash = "sha256:21a8346434fd30e1bc13a26b226fc29b6f33a1cb346d610cbcb4040c3b0e1f63"},
++    {file = "wandb-0.26.0-py3-none-win_arm64.whl", hash = "sha256:99bd11974e9005d3a3f82e1fabfc4909ffa1fdede23a8839f5fbaea2f5be9033"},
++    {file = "wandb-0.26.0.tar.gz", hash = "sha256:0356853895b53fe110e2ed17a1d49c15405498f08e5fbc339deab384f2df45f1"},
++]
++
++[package.dependencies]
++click = ">=8.0.1"
++eval-type-backport = {version = "*", markers = "python_version < \"3.10\""}
++gitpython = ">=1.0.0,<3.1.29 || >3.1.29"
++packaging = "*"
++platformdirs = "*"
++protobuf = ">4.21.0,<5.28.0 || >5.28.0,<5.29.0 || >5.29.0,<8"
++pydantic = "<3"
++pyyaml = "*"
++requests = ">=2.0.0,<3"
++sentry-sdk = ">=2.0.0"
++typing-extensions = ">=4.8,<5"
++
++[package.extras]
++aws = ["boto3", "botocore (>=1.5.76)"]
++azure = ["azure-identity", "azure-storage-blob"]
++gcp = ["google-cloud-storage"]
++importers = ["filelock", "mlflow", "polars (<=1.2.1)", "rich", "tenacity"]
++kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"]
++launch = ["awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore (>=1.5.76)", "chardet", "google-auth", "google-cloud-aiplatform", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "jsonschema", "kubernetes", "kubernetes-asyncio", "nbconvert", "nbformat", "optuna", "pydantic", "pyyaml (>=6.0.0)", "tomli", "tornado (>=6.5.0)", "typing-extensions"]
++media = ["bokeh", "imageio (>=2.28.1)", "moviepy (>=1.0.0)", "numpy", "pillow", "plotly (>=5.18.0)", "rdkit", "soundfile"]
++models = ["cloudpickle"]
++sweeps = ["sweeps (>=0.2.0)"]
++workspaces = ["wandb-workspaces"]
++
+ [[package]]
+ name = "werkzeug"
+ version = "3.1.8"
+@@ -1236,4 +1995,4 @@ type = ["pytest-mypy"]
+ [metadata]
+ lock-version = "2.0"
+ python-versions = "^3.9"
+-content-hash = "4550f4fb0aa9400a7f120968146617d4bc55a86b2922e09584f7f474d5a2e734"
++content-hash = "f11507e147d7c2cb9dd240bbb479024772dae6eaca788addca05b86fe9a8479c"
+diff --git a/ppo.py b/ppo.py
+index ecdb1d4..aa186c9 100644
+--- a/ppo.py
++++ b/ppo.py
+@@ -2,8 +2,62 @@ import argparse
+ import os
+ from distutils.util import strtobool
+ import time
++import random
++import numpy as np
++import gym
++import torch
++import torch.nn as nn
++import torch.optim as optim
++from torch.distributions.categorical import Categorical
+ from torch.utils.tensorboard import SummaryWriter
+ 
++def make_env(gym_id, seed, idx, capture_video, run_name):
++    def thunk():
++        env = gym.make(gym_id)
++        env = gym.wrappers.RecordEpisodeStatistics(env)
++        if capture_video:
++            if idx == 0:
++                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}", episode_trigger=lambda t: t % 1000 == 0)
++        env.seed(seed)
++        env.action_space.seed(seed)
++        env.observation_space.seed(seed)
++        return env
++    return thunk
++
++def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
++    torch.nn.init.orthogonal_(layer.weight, std)
++    torch.nn.init.constant_(layer.bias, bias_const)
++    return layer
++
++class Agent(nn.Module):
++    def __init__(self, envs):
++        super(Agent, self).__init__()
++        self.critic = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,1), std=1.),
++        )
++
++        self.actor = nn.Sequential(
++            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(),64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,64)),
++            nn.Tanh(),
++            layer_init(nn.Linear(64,envs.single_action_space.n), std=0.01),
++        )
++
++    def get_value(self, x):
++        return self.critic(x)
++    
++    def get_action_and_value(self, x, action=None):
++        logits = self.actor(x)
++        probs = Categorical(logits=logits)
++        if action is None:
++            action = probs.sample()
++        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
++
+ def parse_args():
+     parser = argparse.ArgumentParser()
+     parser.add_argument('--exp-name', type=str, default=os.path.basename(__file__).rstrip(".py"),
+@@ -16,21 +70,244 @@ def parse_args():
+         help='seed of the experiment')
+     parser.add_argument('--total-timesteps', type=int, default=25000,
+         help='total timesteps of the experiments')
+-    parser.add_argument('--torch deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++    parser.add_argument('--torch-deterministic', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, `torch.backeds.cudnn.deterministic=False`')
+     parser.add_argument('--cuda', type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
+         help='if toggled, cuda will not be enabled by default')
++    parser.add_argument('--track', type=lambda x:bool(strtobool(x)), default=False, nargs='?', const=True,
++        help='if toggled, this experiment will be tracked with Weights and Biases')
++    parser.add_argument('--wandb-project-name', type=str, default="cleanRL",
++        help="the wandb's project name")
++    parser.add_argument('--wandb-entity', type=str, default=None,
++        help="the entity (team) of wandb's project")
++    parser.add_argument("--num-envs", type=int, default=4,
++    help="the number of parallel game environments")
++    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
++        help="whether to capture videos of the agent performances (check out `videos` folder)")
++    parser.add_argument("--num-steps", type=int, default=128,
++        help="the number of steps to run in each environment per policy rollout")
++    parser.add_argument("--anneal-lr", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggle learning rate annealing for policy and value networks")  
++    parser.add_argument("--gae", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Use GAE for advantage computation")  
++    parser.add_argument('--gamma', type=float, default=0.99,
++        help='the discount factor gamma')
++    parser.add_argument('--gae-lambda', type=float, default=0.95,
++        help='the lambda for the general advantage estimation')
++    parser.add_argument("--num-minibatches", type=int, default=4,
++        help="the number of mini-batches")
++    parser.add_argument("--update-epochs", type=int, default=4,
++        help="the K epochs to update the policy")
++    parser.add_argument("--norm-adv", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles advantages normalization")
++    parser.add_argument('--clip-coef', type=float, default=0.2,
++        help='the surrogate clipping coefficient')     
++    parser.add_argument("--clip-vloss", type=lambda x:bool(strtobool(x)), default=True, nargs='?', const=True,
++        help="Toggles wheter or not to use a clipped loss for the value function, as per the paper")
++    parser.add_argument('--ent-coef', type=float, default=0.01,
++        help='coefficient of the entropy')
++    parser.add_argument('--vf-coef', type=float, default=0.5,
++        help='coefficient of the value function')
++    parser.add_argument('--max-grad-norm', type=float, default=0.5,
++        help='the maximum norm for the gradient clipping')
++    parser.add_argument('--target-kl', type=float, default=None,
++        help='the target KL divergence threshold')
+     args = parser.parse_args()
++    args.batch_size = int(args.num_envs * args.num_steps)
++    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+     return args
+ 
+-    if __name__ == "__main__":
+-        args = parse_args()
+-        print(args)
+-        run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
+-        writer =SummaryWriter(f"runs/{run_name}")
+-        writer.add_text(
+-            "hyperparameters",
+-            "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++if __name__ == "__main__":
++    args = parse_args()
++    print(args)
++    run_name = f"{args.gym_id}_{args.exp_name}_{args.seed}_{int(time.time())}"
++    if args.track:
++        import wandb
++
++        wandb.init(
++            project=args.wandb_project_name,
++            entity=args.wandb_entity,
++            sync_tensorboard=True,
++            config=vars(args),
++            name=run_name,
++            monitor_gym=True,
++            save_code=True,
+         )
+-        for i in range(100):
+-            writer.add_scalar("test_loss", i*2, global_step=i)
+\ No newline at end of file
++    writer =SummaryWriter(f"runs/{run_name}")
++    writer.add_text(
++        "hyperparameters",
++        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key,value in vars(args).items()])),
++    )
++    random.seed(args.seed)
++    np.random.seed(args.seed)
++    torch.manual_seed(args.seed)
++    torch.backends.cudnn.deterministic = args.torch_deterministic
++
++    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
++
++    envs = gym.vector.SyncVectorEnv(
++        [make_env(args.gym_id, args.seed + i, i, args.capture_video, run_name)
++    for i in range(args.num_envs)])
++    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
++
++    agent = Agent(envs).to(device)
++    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
++    
++    
++    # ALGO Logic: Storage setup
++    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
++    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
++    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
++    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
++
++    #TRY NOT TO MODIFY: start the game
++    global_step = 0
++    start_time =time.time()
++    next_obs = torch.Tensor(envs.reset()).to(device)
++    next_done = torch.zeros(args.num_envs).to(device)
++    num_updates = args.total_timesteps // args.batch_size
++
++    for update in range(1, num_updates+1):
++        # Annealing the rate if instructed to do so.abs
++        if args.anneal_lr:
++            frac = 1.0 -(update -1.0) / num_updates
++            lrnow = frac * args.learning_rate
++            optimizer.param_groups[0]["lr"]=lrnow
++        
++        for step in range(0, args.num_steps):
++            global_step += 1 * args.num_envs
++            obs[step] = next_obs
++            dones[step] = next_done
++
++            # ALGO LOGIC : action logic
++            with torch.no_grad():
++                action, logprob, _, value = agent.get_action_and_value(next_obs)
++                values[step] = value.flatten()
++            actions[step] = action
++            logprobs[step] = logprob
++
++            # TRY NOT TO MODIFY: execute the game and log data.abs
++            next_obs, reward, done, info = envs.step(action.cpu().numpy())
++            rewards[step] = torch.tensor(reward).to(device).view(-1)
++            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
++        
++            if isinstance(info, dict) and "episode" in info:
++                for item in info["episode"]:
++                    if item is not None:
++                        print(f"global_step={global_step}, episodic_return={item['r']}")
++                        writer.add_scalar("charts/episodic_return", item["r"], global_step)
++                        writer.add_scalar("charts/episodic_length", item["l"], global_step)
++                        break
++            
++            #bootstrap reward if not done
++            with torch.no_grad():
++                next_value = agent.get_value(next_obs).reshape(1,-1)
++                if args.gae:
++                    advantages = torch.zeros_like(rewards).to(device)
++                    lastgaelam = 0
++                    for t in reversed(range(args.num_steps)):
++                        if t == args.num_steps - 1:
++                            nextnonterminal = 1.0 - next_done
++                            nextvalues = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            nextvalues = values[t+1]
++                        delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
++                        advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
++                    returns =advantages + values
++                else:
++                    returns =torch.zeros_like(rewards).to(device)
++                    for t in reversed(range(args.num_steps)):
++                        if t== args.num_steps - 1:
++                            nextnonterminal = 1.0 -next_done
++                            next_return = next_value
++                        else:
++                            nextnonterminal = 1.0 - dones[t+1]
++                            next_return = returns[t+1]
++                        returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
++                    advantages = returns - values
++            
++            b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
++            b_logprobs =logprobs.reshape(-1)
++            b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
++            b_advantages =advantages.reshape(-1)
++            b_returns = returns.reshape(-1)
++            b_values = values.reshape(-1)
++
++            # Optimizaing the policy and value network
++            b_inds = np.arange(args.batch_size)
++            clipfracs = []
++            for epoch in range(args.update_epochs):
++                np.random.shuffle(b_inds)
++                for start in range(0,args.batch_size, args.minibatch_size):
++                    end = start + args.minibatch_size
++                    mb_inds = b_inds[start:end]
++
++                    _, newlogprob, entropy, newvalue = agent.get_action_and_value( #POSIBLE ERROR AQUI
++                        b_obs[mb_inds], b_actions.long()[mb_inds]
++                    )
++                    logratio = newlogprob - b_logprobs[mb_inds]
++                    ratio = logratio.exp()
++
++                    with torch.no_grad():
++                        #calculate approx kl as in http://joschu.net/blog/kl-aprox.html
++                        old_approx_kl = (-logratio).mean()
++                        approx_kl =((ratio-1) - logratio).mean()
++                        clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean()]
++
++                    mb_advantages = b_advantages[mb_inds]
++                    if args.norm_adv:
++                        mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
++
++                    # Policy loss
++                    pg_loss1 = -mb_advantages * ratio
++                    pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1+ args.clip_coef)
++                    pg_loss = torch.max(pg_loss1, pg_loss2).mean()
++
++                    # Value loss
++                    newvalue = newvalue.view(-1)
++                    if args.clip_vloss:
++                        v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
++                        v_clipped = b_values[mb_inds] + torch.clamp(
++                            newvalue - b_values[mb_inds],
++                            -args.clip_coef,
++                            args.clip_coef
++                        )
++                        v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
++                        v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
++                        v_loss = 0.5 * v_loss_max.mean()
++                    else:
++                        v_loss = 0.5 * ((newvalue - b_returns[mb_inds])**2).mean()
++                    
++                    # Entropy loss
++                    entropy_loss = entropy.mean()
++                    loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
++
++                    optimizer.zero_grad()
++                    loss.backward()
++                    nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
++                    optimizer.step()
++            
++                if args.target_kl is not None:
++                    if approx_kl > args.target_kl:
++                        break
++
++        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
++        var_y = np.var(y_true)
++        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
++
++        # TRY NOT TO MODIFY: record rewards for plotting purposes
++        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
++        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
++        writer.add_scalar("losses/policy_loss",pg_loss.item(), global_step)
++        writer.add_scalar("losses/entropy",entropy_loss.item(), global_step)
++        writer.add_scalar("losses/approx_kl",approx_kl.item(), global_step)
++        writer.add_scalar("losses/clipfrac",np.mean(clipfracs), global_step)
++        writer.add_scalar("losses/explained_variance",explained_var, global_step)
++        print("SPS:", int(global_step / (time.time() - start_time)))
++        writer.add_scalar("charts/SPS", int(global_step / (time.time()- start_time)), global_step)
++
++    envs.close()
++    writer.close()
+\ No newline at end of file
+diff --git a/pyproject.toml b/pyproject.toml
+index 118ee47..d2ae71c 100644
+--- a/pyproject.toml
++++ b/pyproject.toml
+@@ -5,12 +5,19 @@ description = "Implementation of Proximal Policy Optimization (PPO) following th
+ authors = ["Lois Fernández Míguez <loisfernandezcmp@gmail.com>"]
+ license = "MIT"
+ readme = "README.md"
++package-mode = false
+ 
+ [tool.poetry.dependencies]
+ python = "^3.9"
+ gymnasium = "^0.29.1"
+ torch = "^2.1.2"
+ tensorboard = "^2.15.1"
++setuptools = "65.5.0"
++wandb = "^0.26.0"
++gym = "0.25.1"
++numpy = "1.23.5"
++pygame = "^2.6.1"
++imageio-ffmpeg = "^0.6.0"
+ 
+ [tool.poetry.group.dev.dependencies]
+ pytest = "^7.4.3"
diff --git a/wandb/run-20260421_165557-ngbvlof1/files/output.log b/wandb/run-20260421_165557-ngbvlof1/files/output.log
new file mode 100644
index 0000000000000000000000000000000000000000..ee4073871dca082ea3df23a970e7cd0454e5969d
--- /dev/null
+++ b/wandb/run-20260421_165557-ngbvlof1/files/output.log
@@ -0,0 +1,194 @@
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py:329: DeprecationWarning: WARN: Initializing wrapper in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.
+  deprecation(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/step_api_compatibility.py:39: DeprecationWarning: WARN: Initializing environment in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.
+  deprecation(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py:268: DeprecationWarning: WARN: Function `env.seed(seed)` is marked as deprecated and will be removed in the future. Please use `env.reset(seed=seed)` instead.
+  deprecation(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/vector/vector_env.py:56: DeprecationWarning: WARN: Initializing vector env in old step API which returns one bool array instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.
+  deprecation(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/wrappers/monitoring/video_recorder.py:78: DeprecationWarning: WARN: Recording ability for environment CartPole-v1 initialized with `render_mode=None` is marked as deprecated and will be removed in the future.
+  logger.deprecation(
+/home/lois.fernandez@local.eurecat.org/.cache/pypoetry/virtualenvs/lunar2-uqbvMzkl-py3.9/lib/python3.9/site-packages/gym/core.py:51: DeprecationWarning: WARN: The argument mode in render method is deprecated; use render_mode during environment initialization instead.
+See here for more information: https://www.gymlibrary.ml/content/api/
+  deprecation(
+global_step=60, episodic_return=15.0
+global_step=88, episodic_return=22.0
+global_step=120, episodic_return=15.0
+global_step=132, episodic_return=11.0
+global_step=160, episodic_return=25.0
+global_step=172, episodic_return=43.0
+global_step=196, episodic_return=19.0
+global_step=200, episodic_return=17.0
+global_step=232, episodic_return=15.0
+global_step=264, episodic_return=16.0
+global_step=268, episodic_return=27.0
+global_step=276, episodic_return=20.0
+global_step=348, episodic_return=21.0
+global_step=420, episodic_return=47.0
+global_step=428, episodic_return=20.0
+global_step=432, episodic_return=41.0
+global_step=496, episodic_return=16.0
+SPS: 26
+global_step=560, episodic_return=71.0
+global_step=568, episodic_return=18.0
+global_step=608, episodic_return=12.0
+global_step=632, episodic_return=53.0
+global_step=644, episodic_return=19.0
+global_step=668, episodic_return=60.0
+global_step=928, episodic_return=80.0
+global_step=1024, episodic_return=24.0
+SPS: 27
+global_step=1072, episodic_return=110.0
+global_step=1088, episodic_return=105.0
+global_step=1192, episodic_return=42.0
+global_step=1216, episodic_return=36.0
+global_step=1280, episodic_return=16.0
+global_step=1476, episodic_return=137.0
+global_step=1480, episodic_return=50.0
+SPS: 27
+global_step=1660, episodic_return=45.0
+global_step=1812, episodic_return=84.0
+global_step=1896, episodic_return=202.0
+global_step=2008, episodic_return=204.0
+SPS: 27
+global_step=2112, episodic_return=75.0
+global_step=2128, episodic_return=117.0
+SPS: 27
+global_step=2672, episodic_return=136.0
+global_step=2712, episodic_return=150.0
+SPS: 28
+global_step=3356, episodic_return=365.0
+SPS: 28
+global_step=3964, episodic_return=152.0
+global_step=4008, episodic_return=500.0
+SPS: 28
+global_step=4124, episodic_return=363.0
+global_step=4468, episodic_return=439.0
+global_step=4496, episodic_return=133.0
+SPS: 27
+global_step=4728, episodic_return=151.0
+global_step=4784, episodic_return=194.0
+global_step=5116, episodic_return=162.0
+SPS: 26
+global_step=5140, episodic_return=161.0
+global_step=5396, episodic_return=167.0
+global_step=5404, episodic_return=155.0
+SPS: 26
+global_step=5708, episodic_return=148.0
+global_step=5796, episodic_return=164.0
+global_step=5944, episodic_return=135.0
+SPS: 25
+global_step=6148, episodic_return=188.0
+global_step=6404, episodic_return=174.0
+global_step=6504, episodic_return=177.0
+global_step=6616, episodic_return=168.0
+SPS: 25
+global_step=6940, episodic_return=81.0
+global_step=7008, episodic_return=151.0
+global_step=7160, episodic_return=136.0
+SPS: 26
+global_step=7244, episodic_return=185.0
+global_step=7488, episodic_return=137.0
+global_step=7672, episodic_return=166.0
+SPS: 26
+global_step=7808, episodic_return=162.0
+global_step=7840, episodic_return=149.0
+global_step=8072, episodic_return=146.0
+SPS: 26
+global_step=8228, episodic_return=97.0
+global_step=8268, episodic_return=149.0
+global_step=8340, episodic_return=133.0
+global_step=8680, episodic_return=152.0
+SPS: 26
+global_step=8952, episodic_return=153.0
+global_step=8980, episodic_return=188.0
+SPS: 26
+global_step=9312, episodic_return=261.0
+global_step=9548, episodic_return=217.0
+global_step=9700, episodic_return=180.0
+SPS: 26
+global_step=9972, episodic_return=255.0
+global_step=10144, episodic_return=208.0
+global_step=10208, episodic_return=165.0
+SPS: 26
+global_step=10440, episodic_return=185.0
+global_step=10724, episodic_return=188.0
+SPS: 26
+global_step=11156, episodic_return=253.0
+SPS: 26
+global_step=11420, episodic_return=245.0
+global_step=11468, episodic_return=315.0
+global_step=11488, episodic_return=191.0
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new file mode 100644
index 0000000000000000000000000000000000000000..b0923e4766535294adfb22b97e9ca7adf5b7cbad
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+2026-04-21 17:07:51,537 INFO    wandb-AsyncioManager-main:1005872 [mailbox.py:close():155] Closing mailbox, abandoning 1 handles.
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