pushing model

README.md

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+---
+tags:
+- CartPole-v1
+- deep-reinforcement-learning
+- reinforcement-learning
+- custom-implementation
+library_name: cleanrl
+model-index:
+- name: DQN
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: CartPole-v1
+      type: CartPole-v1
+    metrics:
+    - type: mean_reward
+      value: 88.80 +/- 47.92
+      name: mean_reward
+      verified: false
+---
+
+# (CleanRL) **DQN** Agent Playing **CartPole-v1**
+
+This is a trained model of a DQN agent playing CartPole-v1.
+The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
+found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn.py).
+
+## Get Started
+
+To use this model, please install the `cleanrl` package with the following command:
+
+```
+pip install "cleanrl[dqn]"
+python -m cleanrl_utils.enjoy --exp-name dqn --env-id CartPole-v1
+```
+
+Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
+
+
+## Command to reproduce the training
+
+```bash
+curl -OL https://huggingface.co/jacksonhack/CartPole-v1-dqn-seed1/raw/main/dqn.py
+curl -OL https://huggingface.co/jacksonhack/CartPole-v1-dqn-seed1/raw/main/pyproject.toml
+curl -OL https://huggingface.co/jacksonhack/CartPole-v1-dqn-seed1/raw/main/poetry.lock
+poetry install --all-extras
+python dqn.py --save-model --upload-model --total_timesteps 1000
+```
+
+# Hyperparameters
+```python
+{'batch_size': 128,
+ 'buffer_size': 10000,
+ 'capture_video': False,
+ 'cuda': True,
+ 'end_e': 0.05,
+ 'env_id': 'CartPole-v1',
+ 'exp_name': 'dqn',
+ 'exploration_fraction': 0.5,
+ 'gamma': 0.99,
+ 'hf_entity': 'jacksonhack',
+ 'learning_rate': 0.00025,
+ 'learning_starts': 10000,
+ 'num_envs': 1,
+ 'save_model': True,
+ 'seed': 1,
+ 'start_e': 1,
+ 'target_network_frequency': 500,
+ 'tau': 1.0,
+ 'torch_deterministic': True,
+ 'total_timesteps': 1000,
+ 'track': False,
+ 'train_frequency': 10,
+ 'upload_model': True,
+ 'wandb_entity': None,
+ 'wandb_project_name': 'cleanRL'}
+```
+    

dqn.py

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+"""
+*Filename         :dqn.py
+*Description      :
+*Time             :2024/11/13 18:34:33
+*Author           :jackson
+*Version          :1.0
+"""
+
+import os
+import random
+import time
+from dataclasses import dataclass
+
+import gymnasium as gym
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import tyro
+from stable_baselines3.common.buffers import ReplayBuffer
+
+from torch.utils.tensorboard import SummaryWriter
+
+
+@dataclass
+class Args:
+    exp_name: str = os.path.basename(__file__)[: -len(".py")]
+    """the name of this experiment"""
+    seed: int = 1
+    """seed of the experiment"""
+    torch_deterministic: bool = True
+    """if toggled, `torch.backends.cudnn.deterministic=False`"""
+    cuda: bool = True
+    """if toggled, cuda will be enabled by default"""
+    track: bool = False
+    """if toggled, this experiment will be tracked with Weights and Biases"""
+    wandb_project_name: str = "cleanRL"
+    """the wandb's project name"""
+    wandb_entity: str = None
+    """the entity (team) of wandb's project"""
+    capture_video: bool = False
+    """whether to capture videos of the agent performances (check out `videos` folder)"""
+    save_model: bool = False
+    """whether to save model into the `runs/{run_name}` folder"""
+    upload_model: bool = False
+    """whether to upload the saved model to huggingface"""
+    hf_entity: str = "jacksonhack"
+    """the user or org name of the model repository from the Hugging Face Hub"""
+
+    # Algorithm specific arguments
+    env_id: str = "CartPole-v1"
+    """the id of the environment"""
+    total_timesteps: int = 500000
+    """total timesteps of the experiments"""
+    learning_rate: float = 2.5e-4
+    """the learning rate of the optimizer"""
+    num_envs: int = 1
+    """the number of parallel game environments"""
+    buffer_size: int = 10000
+    """the replay memory buffer size"""
+    gamma: float = 0.99
+    """the discount factor gamma"""
+    tau: float = 1.0
+    """the target network update rate"""
+    target_network_frequency: int = 500
+    """the timesteps it takes to update the target network"""
+    batch_size: int = 128
+    """the batch size of sample from the reply memory"""
+    start_e: float = 1
+    """the starting epsilon for exploration"""
+    end_e: float = 0.05
+    """the ending epsilon for exploration"""
+    exploration_fraction: float = 0.5
+    """the fraction of `total-timesteps` it takes from start-e to go end-e"""
+    learning_starts: int = 10000
+    """timestep to start learning"""
+    train_frequency: int = 10
+    """the frequency of training"""
+
+
+def make_env(env_id, seed, idx, capture_video, run_name):
+    def thunk():
+        if capture_video and idx == 0:
+            env = gym.make(env_id, render_mode="rgb_array")
+            env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
+        else:
+            env = gym.make(env_id)
+        env = gym.wrappers.RecordEpisodeStatistics(env)
+        env.action_space.seed(seed)
+
+        return env
+
+    return thunk
+
+
+class QNetwork(nn.Module):
+    def __init__(self, env):
+        super().__init__()
+        self.network = nn.Sequential(
+            nn.Linear(np.array(env.single_observation_space.shape).prod(), 120),
+            nn.ReLU(),
+            nn.Linear(120, 84),
+            nn.ReLU(),
+            nn.Linear(84, env.single_action_space.n),
+        )
+
+    def forward(self, x):
+        return self.network(x)
+
+
+def linear_schedule(start_e: float, end_e: float, duration: int, t: int):
+    slope = (end_e - start_e) / duration
+    return max(slope * t + start_e, end_e)
+
+
+if __name__ == "__main__":
+    import stable_baselines3 as sb3
+
+    if sb3.__version__ < "2.0":
+        raise ValueError(
+            """Ongoing migration: run the following command to install the new dependencies:
+
+            poetry run pip install "stable_baselines3==2.0.0a1"
+    """
+        )
+    args = tyro.cli(Args)
+
+    assert args.num_envs == 1, "vectorized envs are not supported at the moment"
+
+    run_name = f"{args.env_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()])),
+    )
+
+    # TRY NOT TO MODIFY: seeding
+    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 setup
+
+    envs = gym.vector.SyncVectorEnv(
+        [make_env(args.env_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"
+
+    q_network = QNetwork(envs).to(device)
+    optimizer = optim.Adam(q_network.parameters(), lr=args.learning_rate)
+    target_network = QNetwork(envs).to(device)
+    target_network.load_state_dict(q_network.state_dict())
+
+    rb = ReplayBuffer(
+        args.buffer_size,
+        envs.single_observation_space,
+        envs.single_action_space,
+        device,
+        handle_timeout_termination=False,
+    )
+
+    start_time = time.time()
+
+    obs, _ = envs.reset(seed=args.seed)
+
+    for global_step in range(args.total_timesteps):
+        # ALGO LOGIC: put action logic here
+        epsilon = linear_schedule(
+            args.start_e, args.end_e, args.exploration_fraction * args.total_timesteps, global_step
+        )
+
+        if random.random() < epsilon:
+            actions = np.array([envs.single_action_space.sample() for _ in range(envs.num_envs)])
+        else:
+            q_values = q_network(torch.Tensor(obs).to(device))
+            actions = torch.argmax(q_values, dim=1).cpu().numpy()
+
+        # TRY NOT TO MODIFY: execute the game and log data.
+        next_obs, rewards, terminations, truncations, infos = envs.step(actions)
+
+        # TRY NOT TO MODIFY: record rewards for plotting purposes
+        if "final_info" in infos:
+            for info in infos["final_info"]:
+                if info and "episode" in info:
+                    print(f"global_step={global_step}, episodic_return={info['episode']['r']}")
+                    writer.add_scalar("charts/episodic_return", info["episode"]["r"], global_step)
+                    writer.add_scalar("charts/episodic_length", info["episode"]["l"], global_step)
+
+        # TRY NOT TO MODIFY: save data to reply buffer; handle `final_observation`
+        # 向量化环境会自己重置环境
+        real_next_obs = next_obs.copy()
+        for idx, trunc in enumerate(truncations):
+            if trunc:
+                # 将截断状态变为真实状态，确保算法获得更准确的信息
+                real_next_obs[idx] = infos["final_observation"][idx]
+
+        rb.add(obs, real_next_obs, actions, rewards, terminations, infos)
+
+        # TRY NOT TO MODIFY: CRUCIAL step easy to overlook
+        obs = next_obs
+
+        # ALGO LOGIC: training.
+
+        if global_step > args.learning_starts:
+            if global_step % args.train_frequency == 0:
+                data = rb.sample(args.batch_size)
+                with torch.no_grad():
+                    target_max, _ = target_network(data.next_observations).max(dim=1)  # tensor.max() 返回最大值及其索引
+                    td_target = data.rewards.flatten() + args.gamma * target_max * (1 - data.dones.flatten())
+
+                old_val = q_network(data.observations).gather(1, data.actions).squeeze()
+                loss = F.mse_loss(td_target, old_val)
+
+                if global_step % 100 == 0:
+                    writer.add_scalar("losses/td_loss", loss, global_step)
+                    writer.add_scalar("losses/q_values", old_val.mean().item(), global_step)
+                    # SPS: Step per second
+                    print("SPS:", int(global_step / (time.time() - start_time)))
+                    writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step)
+
+                # optimize the model
+                optimizer.zero_grad()
+                loss.backward()
+                optimizer.step()
+
+            # update target network
+            if global_step % args.target_network_frequency == 0:
+                for target_network_param, q_network_param in zip(target_network.parameters(), q_network.parameters()):
+                    target_network_param.data.copy_(
+                        args.tau * q_network_param.data + (1.0 - args.tau) * target_network_param.data
+                    )
+
+    if args.save_model:
+        model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model"
+        torch.save(q_network.state_dict(), model_path)
+        print(f"model saved to {model_path}")
+        from utils.evals.dqn_eval import evaluate
+
+        episodic_returns = evaluate(
+            model_path,
+            make_env,
+            args.env_id,
+            eval_episodes=10,
+            run_name=f"{run_name}-eval",
+            Model=QNetwork,
+            device=device,
+            epsilon=0.05,
+        )
+        for idx, episodic_return in enumerate(episodic_returns):
+            writer.add_scalar("eval/episodic_return", episodic_return, idx)
+
+        if args.upload_model:
+            from utils.huggingface import push_to_hub
+
+            repo_name = f"{args.env_id}-{args.exp_name}-seed{args.seed}"
+            repo_id = f"{args.hf_entity}/{repo_name}" if args.hf_entity else repo_name
+            push_to_hub(args, episodic_returns, repo_id, "DQN", f"runs/{run_name}", f"videos/{run_name}-eval")

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