dqn_jax.py

# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/dqn/#dqn_jaxpy
import argparse
import os
import random
import time
from distutils.util import strtobool

import flax
import flax.linen as nn
import gym
import jax
import jax.numpy as jnp
import numpy as np
import optax
from flax.training.train_state import TrainState
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("--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=2.5e-4,
        help="the learning rate of the optimizer")
    parser.add_argument("--buffer-size", type=int, default=10000,
        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=500,
        help="the timesteps it takes to update the target network")
    parser.add_argument("--batch-size", type=int, default=128,
        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=0.05,
        help="the ending epsilon for exploration")
    parser.add_argument("--exploration-fraction", type=float, default=0.5,
        help="the fraction of `total-timesteps` it takes from start-e to go end-e")
    parser.add_argument("--learning-starts", type=int, default=10000,
        help="timestep to start learning")
    parser.add_argument("--train-frequency", type=int, default=10,
        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):
    action_dim: int

    @nn.compact
    def __call__(self, x: jnp.ndarray):
        x = nn.Dense(120)(x)
        x = nn.relu(x)
        x = nn.Dense(84)(x)
        x = nn.relu(x)
        x = nn.Dense(self.action_dim)(x)
        return x


class TrainState(TrainState):
    target_params: flax.core.FrozenDict


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

        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)
    key = jax.random.PRNGKey(args.seed)
    key, q_key = jax.random.split(key, 2)

    # 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"

    obs = envs.reset()

    q_network = QNetwork(action_dim=envs.single_action_space.n)

    q_state = TrainState.create(
        apply_fn=q_network.apply,
        params=q_network.init(q_key, obs),
        target_params=q_network.init(q_key, obs),
        tx=optax.adam(learning_rate=args.learning_rate),
    )

    q_network.apply = jax.jit(q_network.apply)
    # This step is not necessary as init called on same observation and key will always lead to same initializations
    q_state = q_state.replace(target_params=optax.incremental_update(q_state.params, q_state.target_params, 1))

    rb = ReplayBuffer(
        args.buffer_size,
        envs.single_observation_space,
        envs.single_action_space,
        "cpu",
        handle_timeout_termination=True,
    )

    @jax.jit
    def update(q_state, observations, actions, next_observations, rewards, dones):
        q_next_target = q_network.apply(q_state.target_params, next_observations)  # (batch_size, num_actions)
        q_next_target = jnp.max(q_next_target, axis=-1)  # (batch_size,)
        next_q_value = rewards + (1 - dones) * args.gamma * q_next_target

        def mse_loss(params):
            q_pred = q_network.apply(params, observations)  # (batch_size, num_actions)
            q_pred = q_pred[np.arange(q_pred.shape[0]), actions.squeeze()]  # (batch_size,)
            return ((q_pred - next_q_value) ** 2).mean(), q_pred

        (loss_value, q_pred), grads = jax.value_and_grad(mse_loss, has_aux=True)(q_state.params)
        q_state = q_state.apply_gradients(grads=grads)
        return loss_value, q_pred, q_state

    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:
            q_values = q_network.apply(q_state.params, obs)
            actions = q_values.argmax(axis=-1)
            actions = jax.device_get(actions)

        # 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)
                # perform a gradient-descent step
                loss, old_val, q_state = update(
                    q_state,
                    data.observations.numpy(),
                    data.actions.numpy(),
                    data.next_observations.numpy(),
                    data.rewards.flatten().numpy(),
                    data.dones.flatten().numpy(),
                )

                if global_step % 100 == 0:
                    writer.add_scalar("losses/td_loss", jax.device_get(loss), global_step)
                    writer.add_scalar("losses/q_values", jax.device_get(old_val).mean(), 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)

            # update the target network
            if global_step % args.target_network_frequency == 0:
                q_state = q_state.replace(target_params=optax.incremental_update(q_state.params, q_state.target_params, 1))

    if args.save_model:
        model_path = f"runs/{run_name}/{args.exp_name}.cleanrl_model"
        with open(model_path, "wb") as f:
            f.write(flax.serialization.to_bytes(q_state.params))
        print(f"model saved to {model_path}")
        from cleanrl_utils.evals.dqn_jax_eval import evaluate

        episodic_returns = evaluate(
            model_path,
            make_env,
            args.env_id,
            eval_episodes=10,
            run_name=f"{run_name}-eval",
            Model=QNetwork,
            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, "DQN", f"runs/{run_name}", f"videos/{run_name}-eval")

    envs.close()
    writer.close()