dqn.py

# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/dqn/#dqnpy
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.nn.functional as F
import torch.optim as optim
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="CartPole-v1",
        help="the id of the environment")
    parser.add_argument("--total-timesteps", type=int, default=500000,
        help="total timesteps of the experiments")
    parser.add_argument("--learning-rate", type=float, default=0.0001,
        help="the learning rate of the optimizer")
    parser.add_argument("--max-gradient-norm", type=float, default=1.0,
        help="gradient clipping value")
    parser.add_argument("--buffer-size", type=int, default=300000,
        help="the replay memory buffer size")
    parser.add_argument("--gamma", type=float, default=1.0,
        help="the discount factor gamma")
    parser.add_argument("--target-tau", type=float, default=1.0,
        help="the target network update rate")
    parser.add_argument("--target-network-frequency", type=int, default=100,
        help="the timesteps it takes to update the target network")
    parser.add_argument("--batch-size", type=int, default=256,
        help="the batch size of sample from the reply memory")
    parser.add_argument("--start-e", type=float, default=1.0,
        help="the starting epsilon for exploration")
    parser.add_argument("--end-e", type=float, default=0.1,
        help="the ending epsilon for exploration")
    parser.add_argument("--exploration-fraction", type=float, default=0.2,
        help="the fraction of `total-timesteps` it takes from start-e to go end-e")
    parser.add_argument("--learning-starts", type=int, default=1000,
        help="timestep to start learning")
    parser.add_argument("--train-frequency", type=int, default=1,
        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.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):
        super().__init__()
        self.network = nn.Sequential(
            nn.Linear(np.array(env.single_observation_space.shape).prod(), 512),
            nn.ReLU(),
            nn.Linear(512, 128),
            nn.ReLU(),
            nn.Linear(128, 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__":
    args = parse_args()
    run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
    if args.track:
        import wandb

        args.alg_type = os.path.basename(__file__)
        wandb_sess = wandb.init(
            project=args.wandb_project_name,
            entity=args.wandb_entity,
            config=vars(args),
            save_code=True,
            # group='string',
            name=run_name,
            sync_tensorboard=False,
            monitor_gym=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()])),
    )

    def log_value(name: str, x: float, y: int):
        # writer.add_scalar(name, x, y)
        wandb.log({name: x, "global_step": y})

    # TRY NOT TO MODIFY: seeding
    torch.manual_seed(args.seed)
    # torch.backends.cudnn.deterministic = args.torch_deterministic
    np.random.seed(args.seed)
    random.seed(args.seed)

    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).to(device)
    optimizer = optim.RMSprop(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,
        # optimize_memory_usage=True,
        handle_timeout_termination=True,
    )
    start_time = time.time()
    target_update_counter = 0
    policy_update_counter = 0
    episode_returns = []

    # 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:
            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, dones, infos = envs.step(actions)

        # TRY NOT TO MODIFY: record rewards for plotting purposes
        for info in infos:
            if "episode" in info.keys():
                episode_returns.append(info['episode']['r'])
                episode_returns = episode_returns[-100:]
                print(f"step={global_step}, return={info['episode']['r']}, sps={int(global_step / (time.time() - start_time))}")
                log_value("perf/episodic_return", info["episode"]["r"], global_step)
                log_value("perf/episodic_return_mean_100", np.mean(episode_returns), global_step)
                log_value("perf/episodic_return_std_100", np.std(episode_returns), global_step)
                log_value("debug/episodic_length", info["episode"]["l"], global_step)
                log_value("ex2/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():
                    target_max, _ = target_network(data.next_observations).max(dim=1)
                    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:

                    prev = old_val.detach().cpu().numpy()
                    new = td_target.detach().cpu().numpy()
                    diff, a_diff = new-prev, np.abs(new-prev)

                    mean, a_mean = np.mean(diff), np.mean(a_diff)
                    median, a_median = np.median(diff), np.median(a_diff)
                    maximum, a_maximum = np.max(diff), np.max(a_diff)
                    minimum, a_minimum = np.min(diff), np.min(a_diff)
                    std, a_std = np.std(diff), np.std(a_diff)
                    below, a_below = mean - std, a_mean - a_std
                    above, a_above = mean + std, a_mean + a_std
                    pu_scalar, a_pu_scalar = 2 * mean / maximum, 2 * a_mean / a_maximum
                    policy_frequency_scalar_ratio = 1.0 * pu_scalar
                    a_policy_frequency_scalar_ratio = 1.0 * a_pu_scalar

                    log_value("losses/td_loss", loss, global_step)
                    log_value("losses/q_values", old_val.mean().item(), global_step)
                    log_value("td/mean", mean, global_step)
                    log_value("td/a_mean", a_mean, global_step)
                    log_value("td/median", median, global_step)
                    log_value("td/a_median", a_median, global_step)
                    log_value("td/max", maximum, global_step)
                    log_value("td/a_max", a_maximum, global_step)
                    log_value("td/min", minimum, global_step)
                    log_value("td/a_min", a_minimum, global_step)
                    log_value("td/std", std, global_step)
                    log_value("td/a_std", a_std, global_step)
                    log_value("td/below", below, global_step)
                    log_value("td/a_below", a_below, global_step)
                    log_value("td/above", above, global_step)
                    log_value("td/a_above", a_above, global_step)
                    log_value("alg/pu_scalar", pu_scalar, global_step)
                    log_value("alg/a_pu_scalar", a_pu_scalar, global_step)
                    log_value("alg/policy_frequency_scalar_ratio", policy_frequency_scalar_ratio, global_step)
                    log_value("alg/a_policy_frequency_scalar_ratio", a_policy_frequency_scalar_ratio, global_step)
                    log_value("debug/steps_per_second", int(global_step / (time.time() - start_time)), global_step)

                # optimize the model
                optimizer.zero_grad()
                loss.backward()
                torch.nn.utils.clip_grad_norm_(q_network.parameters(),
                                               args.max_gradient_norm)
                optimizer.step()

            # update target network
            if global_step % args.target_network_frequency == 0:
                target_update_counter += 1
                for target_network_param, q_network_param in zip(target_network.parameters(), q_network.parameters()):
                    target_network_param.data.copy_(
                        args.target_tau * q_network_param.data + (1.0 - args.target_tau) * target_network_param.data
                    )
            policy_update_counter += 1

            if global_step % 100 == 0:
                log_value("alg/n_target_update", target_update_counter, global_step)
                log_value("alg/n_policy_update", policy_update_counter, global_step)

    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 cleanrl_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):
            log_value("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, np.mean(episode_returns), repo_id, "DQN", f"runs/{run_name}", f"videos/{run_name}-eval")

    wandb_sess.finish()
    envs.close()
    writer.close()