PPO_atari.py

# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_ataripy
import os
import random
import time
from dataclasses import dataclass
import ale_py
import gymnasium as gym
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import tyro
from torch.distributions.categorical import Categorical
from torch.utils.tensorboard import SummaryWriter

from atari_wrappers import (  # isort:skip
    ClipRewardEnv,
    EpisodicLifeEnv,
    FireResetEnv,
    MaxAndSkipEnv,
    NoopResetEnv,
)


@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 = True
    """whether to capture videos of the agent performances (check out `videos` folder)"""

    # Algorithm specific arguments
    env_id: str = "SpaceInvadersNoFrameskip-v4"
    """the id of the environment"""
    
    total_timesteps: int = 10_000_000
    learning_rate: float = 2.5e-4
    """the learning rate of the optimizer"""
    num_envs: int = 8
    """the number of parallel game environments"""

    num_steps: int = 128
    """the number of steps to run in each environment per policy rollout"""
    anneal_lr: bool = True
    """Toggle learning rate annealing for policy and value networks"""
    gamma: float = 0.99
    """the discount factor gamma"""
    gae_lambda: float = 0.95
    """the lambda for the general advantage estimation"""
    num_minibatches: int = 4
    """the number of mini-batches"""
    update_epochs: int = 4
    """the K epochs to update the policy"""
    norm_adv: bool = True
    """Toggles advantages normalization"""
    clip_coef: float = 0.1
    """the surrogate clipping coefficient"""
    clip_vloss: bool = True
    """Toggles whether or not to use a clipped loss for the value function, as per the paper."""
    ent_coef: float = 0.01
    """coefficient of the entropy"""
    vf_coef: float = 0.5
    """coefficient of the value function"""
    max_grad_norm: float = 0.5
    """the maximum norm for the gradient clipping"""
    target_kl: float = None
    """the target KL divergence threshold"""

    # to be filled in runtime
    batch_size: int = 0
    """the batch size (computed in runtime)"""
    minibatch_size: int = 0
    """the mini-batch size (computed in runtime)"""
    num_iterations: int = 0
    """the number of iterations (computed in runtime)"""


def make_env(env_id, 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 = NoopResetEnv(env, noop_max=30)
        env = MaxAndSkipEnv(env, skip=4)
        env = EpisodicLifeEnv(env)
        if "FIRE" in env.unwrapped.get_action_meanings():
            env = FireResetEnv(env)
        env = ClipRewardEnv(env)
        env = gym.wrappers.ResizeObservation(env, (84, 84))
        env = gym.wrappers.GrayscaleObservation(env)
        env = gym.wrappers.FrameStackObservation(env, 4)
        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().__init__()
        self.network = nn.Sequential(
            layer_init(nn.Conv2d(4, 32, 8, stride=4)),
            nn.ReLU(),
            layer_init(nn.Conv2d(32, 64, 4, stride=2)),
            nn.ReLU(),
            layer_init(nn.Conv2d(64, 64, 3, stride=1)),
            nn.ReLU(),
            nn.Flatten(),
            layer_init(nn.Linear(64 * 7 * 7, 512)),
            nn.ReLU(),
        )
        self.actor = layer_init(nn.Linear(512, envs.single_action_space.n), std=0.01)
        self.critic = layer_init(nn.Linear(512, 1), std=1)

    def get_value(self, x):
        return self.critic(self.network(x / 255.0))

    def get_action_and_value(self, x, action=None):
        hidden = self.network(x / 255.0)
        logits = self.actor(hidden)
        probs = Categorical(logits=logits)
        if action is None:
            action = probs.sample()
        return action, probs.log_prob(action), probs.entropy(), self.critic(hidden)


if __name__ == "":
    args = tyro.cli(Args)
    args.batch_size = int(args.num_envs * args.num_steps)
    args.minibatch_size = int(args.batch_size // args.num_minibatches)
    args.num_iterations = args.total_timesteps // args.batch_size
    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")
    print("Using device:", device)

    # env setup
    envs = gym.vector.SyncVectorEnv(
        [make_env(args.env_id, 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, _ = envs.reset(seed=args.seed)
    next_obs = torch.Tensor(next_obs).to(device)
    next_done = torch.zeros(args.num_envs).to(device)
    
    # Track episodic returns
    episodic_returns = []
    num_episodes_completed = 0

    for iteration in range(1, args.num_iterations + 1):
        # Save model every 500,000 timesteps and log to wandb
        if global_step % 500_000 == 0 and global_step > 0:
            model_path = f"{run_name}_agent_{global_step}.pth"
            torch.save(agent.state_dict(), model_path)
            if args.track:
                import wandb
                wandb.save(model_path)
        
        # Annealing the rate if instructed to do so.
        if args.anneal_lr:
            frac = 1.0 - (iteration - 1.0) / args.num_iterations
            lrnow = frac * args.learning_rate
            optimizer.param_groups[0]["lr"] = lrnow

        for step in range(0, args.num_steps):
            global_step += 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.
            next_obs, reward, terminations, truncations, infos = envs.step(action.cpu().numpy())
            next_done = np.logical_or(terminations, truncations)
            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)

            if "final_info" in infos:
                for info in infos["final_info"]:
                    if info and "episode" in info:
                        episodic_returns.append(info["episode"]["r"])
                        num_episodes_completed += 1
                        writer.add_scalar("charts/episodic_return", info["episode"]["r"], global_step)
                        writer.add_scalar("charts/episodic_length", info["episode"]["l"], global_step)
                        if args.track:
                            import wandb
                            wandb.log({
                                "charts/episodic_return": info["episode"]["r"],
                                "charts/episodic_length": info["episode"]["l"],
                                "global_step": global_step
                            })
                        print(f"[Step {global_step}] Episode {num_episodes_completed} finished | Return: {info['episode']['r']:.2f} | Length: {info['episode']['l']}")
        # Print progress every 100,000 steps
        if global_step % 100_000 == 0:
            if episodic_returns:
                recent_returns = episodic_returns[-10:] if len(episodic_returns) >= 10 else episodic_returns
                avg_return = np.mean(recent_returns)
                print(f"\n{'='*60}")
                print(f"[Step {global_step}] Progress Update:")
                print(f"  Episodes completed: {num_episodes_completed}")
                print(f"  Avg return (last {len(recent_returns)} episodes): {avg_return:.2f}")
                print(f"{'='*60}\n")
            else:
                print(f"\n[Step {global_step}] No episodes completed yet (this is normal in early training)")

        # Print episode reward after each episode (like trainPPO)
        if episodic_returns:
            avg_reward = np.mean(episodic_returns[-10:])
            running_score = np.mean(episodic_returns[-100:]) if len(episodic_returns) >= 100 else np.mean(episodic_returns)
            print(f"Episode {num_episodes_completed:5d} | Step {global_step:8,} | Score: {episodic_returns[-1]:8.2f} | Avg(10): {avg_reward:8.2f} | Running: {running_score:8.2f}")

        # bootstrap value if not done
        with torch.no_grad():
            next_value = agent.get_value(next_obs).reshape(1, -1)
            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

        # flatten the batch
        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)

        # Optimizing 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(b_obs[mb_inds], b_actions.long()[mb_inds])
                logratio = newlogprob - b_logprobs[mb_inds]
                ratio = logratio.exp()

                with torch.no_grad():
                    old_approx_kl = (-logratio).mean()
                    approx_kl = ((ratio - 1) - logratio).mean()
                    clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean().item()]

                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.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 and 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/old_approx_kl", old_approx_kl.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)
        
        avg_return = np.mean(episodic_returns[-10:]) if episodic_returns else 0.0
        sps = int(global_step / (time.time() - start_time))
        print(f"Iter {iteration} | SPS: {sps} | VLoss: {v_loss.item():.4f} | PLoss: {pg_loss.item():.4f} | Ent: {entropy_loss.item():.4f} | ExpVar: {explained_var:.4f} | AvgRet: {avg_return:.2f} | Episodes: {num_episodes_completed}")
        writer.add_scalar("charts/SPS", sps, global_step)
        if args.track:
            import wandb
            wandb.log({
                "charts/learning_rate": optimizer.param_groups[0]["lr"],
                "losses/value_loss": v_loss.item(),
                "losses/policy_loss": pg_loss.item(),
                "losses/entropy": entropy_loss.item(),
                "losses/old_approx_kl": old_approx_kl.item(),
                "losses/approx_kl": approx_kl.item(),
                "losses/clipfrac": np.mean(clipfracs),
                "losses/explained_variance": explained_var,
                "charts/SPS": sps,
                "avg_return": avg_return,
                "global_step": global_step,
                "episodes_completed": num_episodes_completed
            })

    # Final summary
    print(f"\n{'='*60}")
    print(f"Training Complete!")
    print(f"Total episodes completed: {num_episodes_completed}")
    if episodic_returns:
        final_avg = np.mean(episodic_returns[-100:]) if len(episodic_returns) >= 100 else np.mean(episodic_returns)
        print(f"Final average return: {final_avg:.2f}")
    print(f"{'='*60}\n")
    
    # Save the trained model
    final_model_path = f"{run_name}_agent_final.pth"
    torch.save(agent.state_dict(), final_model_path)
    if args.track:
        import wandb
        wandb.save(final_model_path)
    envs.close()
    writer.close()

def evaluate_agent(model_path, env_id="SpaceInvadersNoFrameskip-v4", num_episodes=10, seed=1, render=False, video_dir="eval_videos2"):
    """
    Loads a PPO agent from model_path and evaluates it for num_episodes.
    Each episode is a full game (all lives), not per life.
    Saves videos to video_dir if specified.
    """
    import os
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    os.makedirs(video_dir, exist_ok=True)
    env = gym.make(env_id, render_mode="rgb_array")
    #env = gym.wrappers.RecordVideo(env, video_dir)
    env = gym.wrappers.RecordEpisodeStatistics(env)
    env = NoopResetEnv(env, noop_max=30)
    env = MaxAndSkipEnv(env)
    # EpisodicLifeEnv is intentionally NOT used here for true game episodes
    if "FIRE" in env.unwrapped.get_action_meanings():
        env = FireResetEnv(env)
    #env = ClipRewardEnv(env)
    env = gym.wrappers.ResizeObservation(env, (84, 84))
    env = gym.wrappers.GrayscaleObservation(env)
    env = gym.wrappers.FrameStackObservation(env, 4)
    env.action_space.seed(seed)
    env.reset(seed=seed)

    # Build agent and load weights
    dummy_envs = gym.vector.SyncVectorEnv([lambda: env])
    agent = Agent(dummy_envs).to(device)
    agent.load_state_dict(torch.load(model_path, map_location=device))
    agent.eval()

    episode_rewards = []
    for ep in range(num_episodes):
        obs, _ = env.reset()
        obs = torch.tensor(obs, dtype=torch.float32, device=device).unsqueeze(0)
        done = False
        total_reward = 0.0
        while not done:
            with torch.no_grad():
                action, _, _, _ = agent.get_action_and_value(obs)
            obs, reward, terminated, truncated, info = env.step(action.cpu().numpy()[0])
            obs = torch.tensor(obs, dtype=torch.float32, device=device).unsqueeze(0)
            done = terminated or truncated
            total_reward += reward
        episode_rewards.append(total_reward)
        print(f"Episode {ep+1}: Reward = {total_reward:.2f}")

    # Plotting the rewards
    try:
        import matplotlib.pyplot as plt
        plt.figure(figsize=(10, 5))
        plt.plot(episode_rewards, marker='o')
        plt.title(f'Rewards over {num_episodes} Episodes')
        plt.xlabel('Episode')
        plt.ylabel('Reward')
        plt.grid(True)
        plt.tight_layout()
        plt.show()
    except ImportError:
        print("matplotlib is not installed. Install it to see reward plots.")

    avg_reward = np.mean(episode_rewards)
    print(f"\nEvaluated {num_episodes} episodes | Average Reward: {avg_reward:.2f}")
    env.close()
    return avg_reward

# New main for evaluation
if __name__ == "__main__":
    model_path = r"D:\Fall25\RL\Assignment-5\Reinforcement-Learning\Assignment-5\SpaceInvadersNoFrameskip-v4__PPO_atari__1__1766540445_agent_final.pth"
    env_id = "SpaceInvadersNoFrameskip-v4"
    num_episodes = 100  # Set to 100 for plotting
    seed = 1
    render = False

    evaluate_agent(
        model_path=model_path,
        env_id=env_id,
        num_episodes=num_episodes,
        seed=seed,
        render=render
    )