Tianshou/examples/box2d/bipedal_hardcore_sac.py

import argparse
import os
import pprint

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

from tianshou.data import Collector, VectorReplayBuffer
from tianshou.env import SubprocVectorEnv
from tianshou.policy import SACPolicy
from tianshou.trainer import offpolicy_trainer
from tianshou.utils import TensorboardLogger
from tianshou.utils.net.common import Net
from tianshou.utils.net.continuous import ActorProb, Critic


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--task', type=str, default="BipedalWalkerHardcore-v3")
    parser.add_argument('--seed', type=int, default=0)
    parser.add_argument('--buffer-size', type=int, default=1000000)
    parser.add_argument('--actor-lr', type=float, default=3e-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=0.1)
    parser.add_argument('--auto-alpha', type=int, default=1)
    parser.add_argument('--alpha-lr', type=float, default=3e-4)
    parser.add_argument('--epoch', type=int, default=100)
    parser.add_argument('--step-per-epoch', type=int, default=100000)
    parser.add_argument('--step-per-collect', type=int, default=10)
    parser.add_argument('--update-per-step', type=float, default=0.1)
    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=10)
    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('--n-step', type=int, default=4)
    parser.add_argument(
        '--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu'
    )
    parser.add_argument('--resume-path', type=str, default=None)
    return parser.parse_args()


class Wrapper(gym.Wrapper):
    """Env wrapper for reward scale, action repeat and removing done penalty"""

    def __init__(self, env, action_repeat=3, reward_scale=5, rm_done=True):
        super().__init__(env)
        self.action_repeat = action_repeat
        self.reward_scale = reward_scale
        self.rm_done = rm_done

    def step(self, action):
        rew_sum = 0.0
        for _ in range(self.action_repeat):
            obs, rew, done, info = self.env.step(action)
            # remove done reward penalty
            if not done or not self.rm_done:
                rew_sum = rew_sum + rew
            if done:
                break
        # scale reward
        return obs, self.reward_scale * rew_sum, done, info


def test_sac_bipedal(args=get_args()):
    env = Wrapper(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]

    train_envs = SubprocVectorEnv(
        [lambda: Wrapper(gym.make(args.task)) for _ in range(args.training_num)]
    )
    # test_envs = gym.make(args.task)
    test_envs = SubprocVectorEnv(
        [
            lambda: Wrapper(gym.make(args.task), reward_scale=1, rm_done=False)
            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_a = Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
    actor = ActorProb(
        net_a,
        args.action_shape,
        max_action=args.max_action,
        device=args.device,
        unbounded=True
    ).to(args.device)
    actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)

    net_c1 = Net(
        args.state_shape,
        args.action_shape,
        hidden_sizes=args.hidden_sizes,
        concat=True,
        device=args.device
    )
    critic1 = Critic(net_c1, device=args.device).to(args.device)
    critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)

    net_c2 = Net(
        args.state_shape,
        args.action_shape,
        hidden_sizes=args.hidden_sizes,
        concat=True,
        device=args.device
    )
    critic2 = Critic(net_c2, device=args.device).to(args.device)
    critic2_optim = torch.optim.Adam(critic2.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 = SACPolicy(
        actor,
        actor_optim,
        critic1,
        critic1_optim,
        critic2,
        critic2_optim,
        tau=args.tau,
        gamma=args.gamma,
        alpha=args.alpha,
        estimation_step=args.n_step,
        action_space=env.action_space
    )
    # load a previous policy
    if args.resume_path:
        policy.load_state_dict(torch.load(args.resume_path))
        print("Loaded agent from: ", args.resume_path)

    # 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.buffer_size)
    # log
    log_path = os.path.join(args.logdir, args.task, 'sac')
    writer = SummaryWriter(log_path)
    logger = TensorboardLogger(writer)

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

    def stop_fn(mean_rewards):
        return mean_rewards >= env.spec.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,
        test_in_train=False,
        stop_fn=stop_fn,
        save_fn=save_fn,
        logger=logger
    )

    if __name__ == '__main__':
        pprint.pprint(result)
        # Let's watch its performance!
        policy.eval()
        test_envs.seed(args.seed)
        test_collector.reset()
        result = test_collector.collect(n_episode=args.test_num, render=args.render)
        rews, lens = result["rews"], result["lens"]
        print(f"Final reward: {rews.mean()}, length: {lens.mean()}")


if __name__ == '__main__':
    test_sac_bipedal()