#!/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 torch.utils.tensorboard import SummaryWriter from tianshou.data import Collector, ReplayBuffer, VectorReplayBuffer from tianshou.policy import SACPolicy from tianshou.trainer import OffpolicyTrainer from tianshou.utils import TensorboardLogger, WandbLogger 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=0.0) 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=get_args()): 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(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( 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, 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 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 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 if args.logger == "wandb": logger = WandbLogger( save_interval=1, name=log_name.replace(os.path.sep, "__"), run_id=args.resume_id, config=args, project=args.wandb_project, ) writer = SummaryWriter(log_path) writer.add_text("args", str(args)) if args.logger == "tensorboard": logger = TensorboardLogger(writer) else: # wandb logger.load(writer) def save_best_fn(policy): 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! 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()