import os import gym import torch import pprint import argparse import numpy as np from torch.utils.tensorboard import SummaryWriter from tianshou.policy import SACPolicy from tianshou.utils import BasicLogger from tianshou.utils.net.common import Net from tianshou.env import SubprocVectorEnv from tianshou.trainer import offpolicy_trainer from tianshou.data import Collector, VectorReplayBuffer from tianshou.utils.net.continuous import ActorProb, Critic def get_args(): parser = argparse.ArgumentParser() parser.add_argument('--task', type=str, default="BipedalWalkerHardcore-v3") parser.add_argument('--seed', type=int, default=0) parser.add_argument('--buffer-size', type=int, default=1000000) parser.add_argument('--actor-lr', type=float, default=3e-4) 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=0.1) parser.add_argument('--auto-alpha', type=int, default=1) parser.add_argument('--alpha-lr', type=float, default=3e-4) parser.add_argument('--epoch', type=int, default=100) parser.add_argument('--step-per-epoch', type=int, default=100000) parser.add_argument('--step-per-collect', type=int, default=10) parser.add_argument('--update-per-step', type=float, default=0.1) parser.add_argument('--batch-size', type=int, default=128) parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[128, 128]) parser.add_argument('--training-num', type=int, default=10) parser.add_argument('--test-num', type=int, default=100) parser.add_argument('--logdir', type=str, default='log') parser.add_argument('--render', type=float, default=0.) parser.add_argument('--n-step', type=int, default=4) 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() class Wrapper(gym.Wrapper): """Env wrapper for reward scale, action repeat and removing done penalty""" def __init__(self, env, action_repeat=3, reward_scale=5, rm_done=True): super().__init__(env) self.action_repeat = action_repeat self.reward_scale = reward_scale self.rm_done = rm_done def step(self, action): r = 0.0 for _ in range(self.action_repeat): obs, reward, done, info = self.env.step(action) # remove done reward penalty if not done or not self.rm_done: r = r + reward if done: break # scale reward return obs, self.reward_scale * r, done, info def test_sac_bipedal(args=get_args()): env = Wrapper(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] train_envs = SubprocVectorEnv([ lambda: Wrapper(gym.make(args.task)) for _ in range(args.training_num)]) # test_envs = gym.make(args.task) test_envs = SubprocVectorEnv([ lambda: Wrapper(gym.make(args.task), reward_scale=1, rm_done=False) 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).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) critic1 = Critic(net_c1, device=args.device).to(args.device) critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr) net_c2 = Net(args.state_shape, args.action_shape, hidden_sizes=args.hidden_sizes, concat=True, device=args.device) 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, action_range=[env.action_space.low[0], env.action_space.high[0]], tau=args.tau, gamma=args.gamma, alpha=args.alpha, estimation_step=args.n_step) # load a previous policy if args.resume_path: policy.load_state_dict(torch.load(args.resume_path)) print("Loaded agent from: ", args.resume_path) # collector train_collector = Collector( policy, train_envs, VectorReplayBuffer(args.buffer_size, len(train_envs)), exploration_noise=True) test_collector = Collector(policy, test_envs) # train_collector.collect(n_step=args.buffer_size) # log log_path = os.path.join(args.logdir, args.task, 'sac') writer = SummaryWriter(log_path) logger = BasicLogger(writer) def save_fn(policy): torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth')) def stop_fn(mean_rewards): return mean_rewards >= env.spec.reward_threshold # 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, update_per_step=args.update_per_step, test_in_train=False, stop_fn=stop_fn, save_fn=save_fn, logger=logger) if __name__ == '__main__': 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) rews, lens = result["rews"], result["lens"] print(f"Final reward: {rews.mean()}, length: {lens.mean()}") if __name__ == '__main__': test_sac_bipedal()