mujoco_sac.py

#!/usr/bin/env python3

import argparse
import datetime
import os
import pprint

import numpy as np
import torch
from mujoco_env import make_mujoco_env

from tianshou.data import Collector, CollectStats, ReplayBuffer, VectorReplayBuffer
from tianshou.highlevel.logger import LoggerFactoryDefault
from tianshou.policy import SACPolicy
from tianshou.policy.base import BasePolicy
from tianshou.trainer import OffpolicyTrainer
from tianshou.utils.net.common import Net
from tianshou.utils.net.continuous import ActorProb, Critic


def get_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument("--task", type=str, default="Ant-v4")
    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=0.0)
    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("--resume-id", type=str, default=None)
    parser.add_argument(
        "--logger",
        type=str,
        default="tensorboard",
        choices=["tensorboard", "wandb"],
    )
    parser.add_argument("--wandb-project", type=str, default="mujoco.benchmark")
    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: argparse.Namespace = get_args()) -> None:
    env, train_envs, test_envs = make_mujoco_env(
        args.task,
        args.seed,
        args.training_num,
        args.test_num,
        obs_norm=False,
    )
    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))
    # seed
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    # model
    net_a = Net(state_shape=args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
    actor = ActorProb(
        net_a,
        args.action_shape,
        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(
        state_shape=args.state_shape,
        action_shape=args.action_shape,
        hidden_sizes=args.hidden_sizes,
        concat=True,
        device=args.device,
    )
    net_c2 = Net(
        state_shape=args.state_shape,
        action_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 = SACPolicy(
        actor=actor,
        actor_optim=actor_optim,
        critic=critic1,
        critic_optim=critic1_optim,
        critic2=critic2,
        critic2_optim=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
    buffer: VectorReplayBuffer | ReplayBuffer
    if args.training_num > 1:
        buffer = VectorReplayBuffer(args.buffer_size, len(train_envs))
    else:
        buffer = ReplayBuffer(args.buffer_size)
    train_collector = Collector[CollectStats](policy, train_envs, buffer, exploration_noise=True)
    test_collector = Collector[CollectStats](policy, test_envs)
    train_collector.reset()
    train_collector.collect(n_step=args.start_timesteps, random=True)

    # log
    now = datetime.datetime.now().strftime("%y%m%d-%H%M%S")
    args.algo_name = "sac"
    log_name = os.path.join(args.task, args.algo_name, str(args.seed), now)
    log_path = os.path.join(args.logdir, log_name)

    # logger
    logger_factory = LoggerFactoryDefault()
    if args.logger == "wandb":
        logger_factory.logger_type = "wandb"
        logger_factory.wandb_project = args.wandb_project
    else:
        logger_factory.logger_type = "tensorboard"

    logger = logger_factory.create_logger(
        log_dir=log_path,
        experiment_name=log_name,
        run_id=args.resume_id,
        config_dict=vars(args),
    )

    def save_best_fn(policy: BasePolicy) -> None:
        torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))

    if not args.watch:
        # trainer
        result = OffpolicyTrainer(
            policy=policy,
            train_collector=train_collector,
            test_collector=test_collector,
            max_epoch=args.epoch,
            step_per_epoch=args.step_per_epoch,
            step_per_collect=args.step_per_collect,
            episode_per_test=args.test_num,
            batch_size=args.batch_size,
            save_best_fn=save_best_fn,
            logger=logger,
            update_per_step=args.update_per_step,
            test_in_train=False,
        ).run()
        pprint.pprint(result)

    # Let's watch its performance!
    test_envs.seed(args.seed)
    test_collector.reset()
    collector_stats = test_collector.collect(n_episode=args.test_num, render=args.render)
    print(collector_stats)


if __name__ == "__main__":
    test_sac()