mujoco_sac.py

#!/usr/bin/env python3

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

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


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 = BasicLogger(writer)

    def save_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_fn=save_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()