Tianshou/test/continuous/test_redq.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.policy import REDQPolicy
from tianshou.trainer import offpolicy_trainer
from tianshou.utils import TensorboardLogger
from tianshou.utils.net.common import EnsembleLinear, Net
from tianshou.utils.net.continuous import ActorProb, Critic


def get_args():
    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('--ensemble-size', type=int, default=4)
    parser.add_argument('--subset-size', type=int, default=2)
    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('--alpha', type=float, default=0.2)
    parser.add_argument('--auto-alpha', action='store_true', default=False)
    parser.add_argument('--alpha-lr', type=float, default=3e-4)
    parser.add_argument("--start-timesteps", type=int, default=1000)
    parser.add_argument('--epoch', type=int, default=5)
    parser.add_argument('--step-per-epoch', type=int, default=5000)
    parser.add_argument('--step-per-collect', type=int, default=1)
    parser.add_argument('--update-per-step', type=int, default=3)
    parser.add_argument('--n-step', type=int, default=1)
    parser.add_argument('--batch-size', type=int, default=64)
    parser.add_argument(
        '--target-mode', type=str, choices=('min', 'mean'), default='min'
    )
    parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[64, 64])
    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.)
    parser.add_argument(
        '--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu'
    )
    args = parser.parse_known_args()[0]
    return args


def test_redq(args=get_args()):
    env = gym.make(args.task)
    args.state_shape = env.observation_space.shape or env.observation_space.n
    args.action_shape = env.action_space.shape or env.action_space.n
    args.max_action = env.action_space.high[0]
    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
        )
    # 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(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
    actor = ActorProb(
        net,
        args.action_shape,
        device=args.device,
        unbounded=True,
        conditioned_sigma=True
    ).to(args.device)
    actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)

    def linear(x, y):
        return EnsembleLinear(args.ensemble_size, x, y)

    net_c = Net(
        args.state_shape,
        args.action_shape,
        hidden_sizes=args.hidden_sizes,
        concat=True,
        device=args.device,
        linear_layer=linear,
    )
    critic = Critic(
        net_c, device=args.device, linear_layer=linear, flatten_input=False
    ).to(args.device)
    critic_optim = torch.optim.Adam(critic.parameters(), lr=args.critic_lr)

    if args.auto_alpha:
        target_entropy = -np.prod(env.action_space.shape)
        log_alpha = torch.zeros(1, requires_grad=True, device=args.device)
        alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr)
        args.alpha = (target_entropy, log_alpha, alpha_optim)

    policy = REDQPolicy(
        actor,
        actor_optim,
        critic,
        critic_optim,
        args.ensemble_size,
        args.subset_size,
        tau=args.tau,
        gamma=args.gamma,
        alpha=args.alpha,
        estimation_step=args.n_step,
        actor_delay=args.update_per_step,
        target_mode=args.target_mode,
        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)
    train_collector.collect(n_step=args.start_timesteps, random=True)
    # log
    log_path = os.path.join(args.logdir, args.task, 'redq')
    writer = SummaryWriter(log_path)
    logger = TensorboardLogger(writer)

    def save_best_fn(policy):
        torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))

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

    # trainer
    result = offpolicy_trainer(
        policy,
        train_collector,
        test_collector,
        args.epoch,
        args.step_per_epoch,
        args.step_per_collect,
        args.test_num,
        args.batch_size,
        update_per_step=args.update_per_step,
        stop_fn=stop_fn,
        save_best_fn=save_best_fn,
        logger=logger
    )
    assert stop_fn(result['best_reward'])

    if __name__ == '__main__':
        pprint.pprint(result)
        # Let's watch its performance!
        env = gym.make(args.task)
        policy.eval()
        collector = Collector(policy, env)
        result = collector.collect(n_episode=1, render=args.render)
        rews, lens = result["rews"], result["lens"]
        print(f"Final reward: {rews.mean()}, length: {lens.mean()}")


if __name__ == '__main__':
    test_redq()