Tianshou/examples/mujoco/mujoco_ddpg.py

#!/usr/bin/env python3

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

from tianshou.policy import DDPGPolicy
from tianshou.utils import BasicLogger
from tianshou.env import SubprocVectorEnv
from tianshou.utils.net.common import Net
from tianshou.exploration import GaussianNoise
from tianshou.trainer import offpolicy_trainer
from tianshou.utils.net.continuous import Actor, 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('--exploration-noise', type=float, default=0.1)
    parser.add_argument("--start-timesteps", type=int, default=25000)
    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)
    return parser.parse_args()


def test_ddpg(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]
    args.exploration_noise = args.exploration_noise * args.max_action
    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 = Actor(
        net_a, args.action_shape, max_action=args.max_action,
        device=args.device).to(args.device)
    actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
    net_c = Net(args.state_shape, args.action_shape,
                hidden_sizes=args.hidden_sizes,
                concat=True, device=args.device)
    critic = Critic(net_c, device=args.device).to(args.device)
    critic_optim = torch.optim.Adam(critic.parameters(), lr=args.critic_lr)
    policy = DDPGPolicy(
        actor, actor_optim, critic, critic_optim,
        tau=args.tau, gamma=args.gamma,
        exploration_noise=GaussianNoise(sigma=args.exploration_noise),
        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
    log_path = os.path.join(args.logdir, args.task, 'ddpg', 'seed_' + str(args.seed) +
                            '_' + datetime.datetime.now().strftime('%m%d_%H%M%S') +
                            '-' + args.task.replace('-', '_') + '_ddpg')
    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'))

    # 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)

    # 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_ddpg()