import argparse import os import pprint import gym import numpy as np import torch from torch.utils.tensorboard import SummaryWriter from tianshou.data import Collector, VectorReplayBuffer from tianshou.env import SubprocVectorEnv from tianshou.policy import SACPolicy from tianshou.trainer import offpolicy_trainer from tianshou.utils import TensorboardLogger 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="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): rew_sum = 0.0 for _ in range(self.action_repeat): obs, rew, done, info = self.env.step(action) # remove done reward penalty if not done or not self.rm_done: rew_sum = rew_sum + rew if done: break # scale reward return obs, self.reward_scale * rew_sum, 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, 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)) 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 = TensorboardLogger(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()