Tianshou/examples/mujoco/mujoco_sac.py

import os
import gym
import torch
import pprint
import argparse
import numpy as np
from torch.utils.tensorboard import SummaryWriter

from tianshou.policy import SACPolicy
from tianshou.env import SubprocVectorEnv
from tianshou.utils.net.common import Net
from tianshou.trainer import offpolicy_trainer
from tianshou.data import Collector, ReplayBuffer
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=1626)
    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=3e-4)
    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('--n-step', type=int, default=2)
    parser.add_argument('--epoch', type=int, default=100)
    parser.add_argument('--step-per-epoch', type=int, default=10000)
    parser.add_argument('--collect-per-step', type=int, default=4)
    parser.add_argument('--update-per-step', type=int, default=1)
    parser.add_argument('--pre-collect-step', type=int, default=10000)
    parser.add_argument('--batch-size', type=int, default=256)
    parser.add_argument('--hidden-layer-size', type=int, default=256)
    parser.add_argument('--layer-num', type=int, default=1)
    parser.add_argument('--training-num', type=int, default=16)
    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('--log-interval', type=int, default=1000)
    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)
    train_envs = SubprocVectorEnv(
        [lambda: gym.make(args.task) for _ in range(args.training_num)])
    # 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 = Net(args.layer_num, args.state_shape, device=args.device,
              hidden_layer_size=args.hidden_layer_size)
    actor = ActorProb(
        net, args.action_shape, args.max_action, args.device, unbounded=True,
        hidden_layer_size=args.hidden_layer_size, conditioned_sigma=True,
    ).to(args.device)
    actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
    net_c1 = Net(args.layer_num, args.state_shape, args.action_shape,
                 concat=True, device=args.device,
                 hidden_layer_size=args.hidden_layer_size)
    critic1 = Critic(
        net_c1, args.device, hidden_layer_size=args.hidden_layer_size
    ).to(args.device)
    critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
    net_c2 = Net(args.layer_num, args.state_shape, args.action_shape,
                 concat=True, device=args.device,
                 hidden_layer_size=args.hidden_layer_size)
    critic2 = Critic(
        net_c2, args.device, hidden_layer_size=args.hidden_layer_size
    ).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,
        action_range=[env.action_space.low[0], env.action_space.high[0]],
        tau=args.tau, gamma=args.gamma, alpha=args.alpha,
        estimation_step=args.n_step)
    # 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
    train_collector = Collector(
        policy, train_envs, ReplayBuffer(args.buffer_size))
    test_collector = Collector(policy, test_envs)
    # log
    log_path = os.path.join(args.logdir, args.task, 'sac')
    writer = SummaryWriter(log_path)

    def watch():
        # watch agent's performance
        print("Testing agent ...")
        policy.eval()
        test_envs.seed(args.seed)
        test_collector.reset()
        result = test_collector.collect(n_episode=[1] * args.test_num,
                                        render=args.render)
        pprint.pprint(result)

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

    def stop_fn(mean_rewards):
        return False

    if args.watch:
        watch()
        exit(0)

    # trainer
    train_collector.collect(n_step=args.pre_collect_step, random=True)
    result = offpolicy_trainer(
        policy, train_collector, test_collector, args.epoch,
        args.step_per_epoch, args.collect_per_step, args.test_num,
        args.batch_size, args.update_per_step,
        stop_fn=stop_fn, save_fn=save_fn, writer=writer,
        log_interval=args.log_interval)
    pprint.pprint(result)
    watch()


if __name__ == '__main__':
    test_sac()