#!/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 TensorboardLogger 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 = TensorboardLogger(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()