Tianshou/examples/mujoco/mujoco_sac.py

#!/usr/bin/env python3

import argparse
import datetime
import os
import pprint

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

from tianshou.data import Collector, ReplayBuffer, 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='Ant-v3')
    parser.add_argument('--seed', type=int, default=0)
    parser.add_argument('--buffer-size', type=int, default=1000000)
    parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[256, 256])
    parser.add_argument('--actor-lr', type=float, default=1e-3)
    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', default=False, action='store_true')
    parser.add_argument('--alpha-lr', type=float, default=3e-4)
    parser.add_argument("--start-timesteps", type=int, default=10000)
    parser.add_argument('--epoch', type=int, default=200)
    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=1)
    parser.add_argument('--n-step', type=int, default=1)
    parser.add_argument('--batch-size', type=int, default=256)
    parser.add_argument('--training-num', type=int, default=1)
    parser.add_argument('--test-num', type=int, default=10)
    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'
    )
    parser.add_argument('--resume-path', type=str, default=None)
    parser.add_argument(
        '--watch',
        default=False,
        action='store_true',
        help='watch the play of pre-trained policy only'
    )
    return parser.parse_args()


def test_sac(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]
    print("Observations shape:", args.state_shape)
    print("Actions shape:", args.action_shape)
    print("Action range:", np.min(env.action_space.low), np.max(env.action_space.high))
    # train_envs = gym.make(args.task)
    if args.training_num > 1:
        train_envs = SubprocVectorEnv(
            [lambda: gym.make(args.task) for _ in range(args.training_num)]
        )
    else:
        train_envs = gym.make(args.task)
    # test_envs = gym.make(args.task)
    test_envs = SubprocVectorEnv(
        [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_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,
        conditioned_sigma=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
    )
    net_c2 = 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)
    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, map_location=args.device))
        print("Loaded agent from: ", args.resume_path)

    # collector
    if args.training_num > 1:
        buffer = VectorReplayBuffer(args.buffer_size, len(train_envs))
    else:
        buffer = ReplayBuffer(args.buffer_size)
    train_collector = Collector(policy, train_envs, buffer, exploration_noise=True)
    test_collector = Collector(policy, test_envs)
    train_collector.collect(n_step=args.start_timesteps, random=True)
    # log
    t0 = datetime.datetime.now().strftime("%m%d_%H%M%S")
    log_file = f'seed_{args.seed}_{t0}-{args.task.replace("-", "_")}_sac'
    log_path = os.path.join(args.logdir, args.task, 'sac', log_file)
    writer = SummaryWriter(log_path)
    writer.add_text("args", str(args))
    logger = TensorboardLogger(writer)

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

    if not args.watch:
        # 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,
            save_best_fn=save_best_fn,
            logger=logger,
            update_per_step=args.update_per_step,
            test_in_train=False
        )
        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)
    print(f'Final reward: {result["rews"].mean()}, length: {result["lens"].mean()}')


if __name__ == '__main__':
    test_sac()