c51_atari.py

# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/c51/#c51_ataripy
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 stable_baselines3.common.atari_wrappers import (
    ClipRewardEnv,
    EpisodicLifeEnv,
    FireResetEnv,
    MaxAndSkipEnv,
    NoopResetEnv,
)
from stable_baselines3.common.buffers import ReplayBuffer
from torch.utils.tensorboard import SummaryWriter


def parse_args():
    # fmt: off
    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("--seed", type=int, default=1,
        help="seed of the experiment")
    parser.add_argument("--torch-deterministic", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
        help="if toggled, `torch.backends.cudnn.deterministic=False`")
    parser.add_argument("--cuda", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
        help="if toggled, cuda will 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("--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("--save-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
        help="whether to save model into the `runs/{run_name}` folder")
    parser.add_argument("--upload-model", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
        help="whether to upload the saved model to huggingface")
    parser.add_argument("--hf-entity", type=str, default="",
        help="the user or org name of the model repository from the Hugging Face Hub")

    # Algorithm specific arguments
    parser.add_argument("--env-id", type=str, default="BreakoutNoFrameskip-v4",
        help="the id of the environment")
    parser.add_argument("--total-timesteps", type=int, default=10000000,
        help="total timesteps of the experiments")
    parser.add_argument("--learning-rate", type=float, default=2.5e-4,
        help="the learning rate of the optimizer")
    parser.add_argument("--n-atoms", type=int, default=51,
        help="the number of atoms")
    parser.add_argument("--v-min", type=float, default=-10,
        help="the number of atoms")
    parser.add_argument("--v-max", type=float, default=10,
        help="the number of atoms")
    parser.add_argument("--buffer-size", type=int, default=1000000,
        help="the replay memory buffer size")
    parser.add_argument("--gamma", type=float, default=0.99,
        help="the discount factor gamma")
    parser.add_argument("--target-network-frequency", type=int, default=10000,
        help="the timesteps it takes to update the target network")
    parser.add_argument("--batch-size", type=int, default=32,
        help="the batch size of sample from the reply memory")
    parser.add_argument("--start-e", type=float, default=1,
        help="the starting epsilon for exploration")
    parser.add_argument("--end-e", type=float, default=0.01,
        help="the ending epsilon for exploration")
    parser.add_argument("--exploration-fraction", type=float, default=0.1,
        help="the fraction of `total-timesteps` it takes from start-e to go end-e")
    parser.add_argument("--learning-starts", type=int, default=80000,
        help="timestep to start learning")
    parser.add_argument("--train-frequency", type=int, default=4,
        help="the frequency of training")
    args = parser.parse_args()
    # fmt: on
    return args


def make_env(env_id, seed, idx, capture_video, run_name):
    def thunk():
        env = gym.make(env_id)
        env = gym.wrappers.RecordEpisodeStatistics(env)
        if capture_video:
            if idx == 0:
                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
        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.FrameStack(env, 4)
        env.seed(seed)
        env.action_space.seed(seed)
        env.observation_space.seed(seed)
        return env

    return thunk


# ALGO LOGIC: initialize agent here:
class QNetwork(nn.Module):
    def __init__(self, env, n_atoms=101, v_min=-100, v_max=100):
        super().__init__()
        self.env = env
        self.n_atoms = n_atoms
        self.register_buffer("atoms", torch.linspace(v_min, v_max, steps=n_atoms))
        self.n = env.single_action_space.n
        self.network = nn.Sequential(
            nn.Conv2d(4, 32, 8, stride=4),
            nn.ReLU(),
            nn.Conv2d(32, 64, 4, stride=2),
            nn.ReLU(),
            nn.Conv2d(64, 64, 3, stride=1),
            nn.ReLU(),
            nn.Flatten(),
            nn.Linear(3136, 512),
            nn.ReLU(),
            nn.Linear(512, self.n * n_atoms),
        )

    def get_action(self, x, action=None):
        logits = self.network(x / 255.0)
        # probability mass function for each action
        pmfs = torch.softmax(logits.view(len(x), self.n, self.n_atoms), dim=2)
        q_values = (pmfs * self.atoms).sum(2)
        if action is None:
            action = torch.argmax(q_values, 1)
        return action, pmfs[torch.arange(len(x)), action]


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__":
    args = parse_args()
    # run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
    run_name = "PongNoFrameskip-v4__c51_atari__1__1672771568"
    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, 0, args.capture_video, run_name)])
    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"

    q_network = QNetwork(envs, n_atoms=args.n_atoms, v_min=args.v_min, v_max=args.v_max).to(device)
    optimizer = optim.Adam(q_network.parameters(), lr=args.learning_rate, eps=0.01 / args.batch_size)
    target_network = QNetwork(envs, n_atoms=args.n_atoms, v_min=args.v_min, v_max=args.v_max).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,
        optimize_memory_usage=True,
        handle_timeout_termination=True,
    )
    # start_time = time.time()

    # # TRY NOT TO MODIFY: start the game
    # obs = envs.reset()
    # 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:
    #         actions, pmf = q_network.get_action(torch.Tensor(obs).to(device))
    #         actions = actions.cpu().numpy()

    #     # TRY NOT TO MODIFY: execute the game and log data.
    #     next_obs, rewards, dones, infos = envs.step(actions)

    #     # TRY NOT TO MODIFY: record rewards for plotting purposes
    #     for info in infos:
    #         if "episode" in info.keys():
    #             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)
    #             writer.add_scalar("charts/epsilon", epsilon, global_step)
    #             break

    #     # TRY NOT TO MODIFY: save data to reply buffer; handle `terminal_observation`
    #     real_next_obs = next_obs.copy()
    #     for idx, d in enumerate(dones):
    #         if d:
    #             real_next_obs[idx] = infos[idx]["terminal_observation"]
    #     rb.add(obs, real_next_obs, actions, rewards, dones, 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():
    #                 _, next_pmfs = target_network.get_action(data.next_observations)
    #                 next_atoms = data.rewards + args.gamma * target_network.atoms * (1 - data.dones)
    #                 # projection
    #                 delta_z = target_network.atoms[1] - target_network.atoms[0]
    #                 tz = next_atoms.clamp(args.v_min, args.v_max)

    #                 b = (tz - args.v_min) / delta_z
    #                 l = b.floor().clamp(0, args.n_atoms - 1)
    #                 u = b.ceil().clamp(0, args.n_atoms - 1)
    #                 # (l == u).float() handles the case where bj is exactly an integer
    #                 # example bj = 1, then the upper ceiling should be uj= 2, and lj= 1
    #                 d_m_l = (u + (l == u).float() - b) * next_pmfs
    #                 d_m_u = (b - l) * next_pmfs
    #                 target_pmfs = torch.zeros_like(next_pmfs)
    #                 for i in range(target_pmfs.size(0)):
    #                     target_pmfs[i].index_add_(0, l[i].long(), d_m_l[i])
    #                     target_pmfs[i].index_add_(0, u[i].long(), d_m_u[i])

    #             _, old_pmfs = q_network.get_action(data.observations, data.actions.flatten())
    #             loss = (-(target_pmfs * old_pmfs.clamp(min=1e-5, max=1 - 1e-5).log()).sum(-1)).mean()

    #             if global_step % 100 == 0:
    #                 writer.add_scalar("losses/loss", loss.item(), global_step)
    #                 old_val = (old_pmfs * q_network.atoms).sum(1)
    #                 writer.add_scalar("losses/q_values", old_val.mean().item(), 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)

    #             # optimize the model
    #             optimizer.zero_grad()
    #             loss.backward()
    #             optimizer.step()

    #         # update the target network
    #         if global_step % args.target_network_frequency == 0:
    #             target_network.load_state_dict(q_network.state_dict())

    if args.save_model:
        model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model"
        # model_data = {
        #     "model_weights": q_network.state_dict(),
        #     "args": vars(args),
        # }
        # torch.save(model_data, model_path)
        print(f"model saved to {model_path}")
        from cleanrl_utils.evals.c51_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 cleanrl_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, "C51", f"runs/{run_name}", f"videos/{run_name}-eval")

    envs.close()
    writer.close()