Tianshou/test/continuous/test_ddpg.py

import argparse
import os
import pprint

import gymnasium as gym
import numpy as np
import torch
from torch.utils.tensorboard import SummaryWriter

from tianshou.data import Collector, VectorReplayBuffer
from tianshou.env import DummyVectorEnv
from tianshou.exploration import GaussianNoise
from tianshou.policy import DDPGPolicy
from tianshou.policy.base import BasePolicy
from tianshou.trainer import OffpolicyTrainer
from tianshou.utils import TensorboardLogger
from tianshou.utils.net.common import Net
from tianshou.utils.net.continuous import Actor, Critic
from tianshou.utils.space_info import SpaceInfo


def get_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument("--task", type=str, default="Pendulum-v1")
    parser.add_argument("--reward-threshold", type=float, default=None)
    parser.add_argument("--seed", type=int, default=0)
    parser.add_argument("--buffer-size", type=int, default=20000)
    parser.add_argument("--actor-lr", type=float, default=1e-4)
    parser.add_argument("--critic-lr", type=float, default=1e-3)
    parser.add_argument("--gamma", type=float, default=0.99)
    parser.add_argument("--tau", type=float, default=0.005)
    parser.add_argument("--exploration-noise", type=float, default=0.1)
    parser.add_argument("--epoch", type=int, default=5)
    parser.add_argument("--step-per-epoch", type=int, default=20000)
    parser.add_argument("--step-per-collect", type=int, default=8)
    parser.add_argument("--update-per-step", type=float, default=0.125)
    parser.add_argument("--batch-size", type=int, default=128)
    parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[128, 128])
    parser.add_argument("--training-num", type=int, default=8)
    parser.add_argument("--test-num", type=int, default=100)
    parser.add_argument("--logdir", type=str, default="log")
    parser.add_argument("--render", type=float, default=0.0)
    parser.add_argument("--rew-norm", action="store_true", default=False)
    parser.add_argument("--n-step", type=int, default=3)
    parser.add_argument(
        "--device",
        type=str,
        default="cuda" if torch.cuda.is_available() else "cpu",
    )
    return parser.parse_known_args()[0]


def test_ddpg(args: argparse.Namespace = get_args()) -> None:
    env = gym.make(args.task)
    space_info = SpaceInfo.from_env(env)
    args.state_shape = space_info.observation_info.obs_shape
    args.action_shape = space_info.action_info.action_shape
    args.max_action = space_info.action_info.max_action
    if args.reward_threshold is None:
        default_reward_threshold = {"Pendulum-v0": -250, "Pendulum-v1": -250}
        args.reward_threshold = default_reward_threshold.get(
            args.task,
            env.spec.reward_threshold if env.spec else None,
        )
    # you can also use tianshou.env.SubprocVectorEnv
    # train_envs = gym.make(args.task)
    train_envs = DummyVectorEnv([lambda: gym.make(args.task) for _ in range(args.training_num)])
    # test_envs = gym.make(args.task)
    test_envs = DummyVectorEnv([lambda: gym.make(args.task) for _ in range(args.test_num)])
    # seed
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    train_envs.seed(args.seed)
    test_envs.seed(args.seed)
    # model
    net = Net(state_shape=args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
    actor = Actor(net, args.action_shape, max_action=args.max_action, device=args.device).to(
        args.device,
    )
    actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
    net = Net(
        state_shape=args.state_shape,
        action_shape=args.action_shape,
        hidden_sizes=args.hidden_sizes,
        concat=True,
        device=args.device,
    )
    critic = Critic(net, device=args.device).to(args.device)
    critic_optim = torch.optim.Adam(critic.parameters(), lr=args.critic_lr)
    policy: DDPGPolicy = DDPGPolicy(
        actor=actor,
        actor_optim=actor_optim,
        critic=critic,
        critic_optim=critic_optim,
        tau=args.tau,
        gamma=args.gamma,
        exploration_noise=GaussianNoise(sigma=args.exploration_noise),
        estimation_step=args.n_step,
        action_space=env.action_space,
    )
    # collector
    train_collector = Collector(
        policy,
        train_envs,
        VectorReplayBuffer(args.buffer_size, len(train_envs)),
        exploration_noise=True,
    )
    test_collector = Collector(policy, test_envs)
    # log
    log_path = os.path.join(args.logdir, args.task, "ddpg")
    writer = SummaryWriter(log_path)
    logger = TensorboardLogger(writer)

    def save_best_fn(policy: BasePolicy) -> None:
        torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))

    def stop_fn(mean_rewards: float) -> bool:
        return mean_rewards >= args.reward_threshold

    # trainer
    result = OffpolicyTrainer(
        policy=policy,
        train_collector=train_collector,
        test_collector=test_collector,
        max_epoch=args.epoch,
        step_per_epoch=args.step_per_epoch,
        step_per_collect=args.step_per_collect,
        episode_per_test=args.test_num,
        batch_size=args.batch_size,
        update_per_step=args.update_per_step,
        stop_fn=stop_fn,
        save_best_fn=save_best_fn,
        logger=logger,
    ).run()
    assert stop_fn(result.best_reward)

    if __name__ == "__main__":
        pprint.pprint(result)
        # Let's watch its performance!
        env = gym.make(args.task)
        collector = Collector(policy, env)
        collector_stats = collector.collect(n_episode=1, render=args.render, is_eval=True)
        print(collector_stats)


if __name__ == "__main__":
    test_ddpg()