import gym import torch import argparse import numpy as np from torch import nn from torch.utils.tensorboard import SummaryWriter from tianshou.policy import DDPGPolicy from tianshou.trainer import step_trainer from tianshou.data import Collector, ReplayBuffer from tianshou.env import VectorEnv, SubprocVectorEnv class Actor(nn.Module): def __init__(self, layer_num, state_shape, action_shape, max_action, device='cpu'): super().__init__() self.device = device self.model = [ nn.Linear(np.prod(state_shape), 128), nn.ReLU(inplace=True)] for i in range(layer_num): self.model += [nn.Linear(128, 128), nn.ReLU(inplace=True)] self.model += [nn.Linear(128, np.prod(action_shape))] self.model = nn.Sequential(*self.model) self._max = max_action def forward(self, s, **kwargs): s = torch.tensor(s, device=self.device, dtype=torch.float) batch = s.shape[0] s = s.view(batch, -1) logits = self.model(s) logits = self._max * torch.tanh(logits) return logits, None class Critic(nn.Module): def __init__(self, layer_num, state_shape, action_shape, device='cpu'): super().__init__() self.device = device self.model = [ nn.Linear(np.prod(state_shape) + np.prod(action_shape), 128), nn.ReLU(inplace=True)] for i in range(layer_num): self.model += [nn.Linear(128, 128), nn.ReLU(inplace=True)] self.model += [nn.Linear(128, 1)] self.model = nn.Sequential(*self.model) def forward(self, s, a): s = torch.tensor(s, device=self.device, dtype=torch.float) if isinstance(a, np.ndarray): a = torch.tensor(a, device=self.device, dtype=torch.float) batch = s.shape[0] s = s.view(batch, -1) a = a.view(batch, -1) logits = self.model(torch.cat([s, a], dim=1)) return logits def get_args(): parser = argparse.ArgumentParser() parser.add_argument('--task', type=str, default='Pendulum-v0') parser.add_argument('--seed', type=int, default=1626) parser.add_argument('--buffer-size', type=int, default=20000) parser.add_argument('--actor-lr', type=float, default=1e-4) parser.add_argument('--actor-wd', type=float, default=0) parser.add_argument('--critic-lr', type=float, default=1e-3) parser.add_argument('--critic-wd', type=float, default=1e-2) parser.add_argument('--gamma', type=float, default=0.99) parser.add_argument('--tau', type=float, default=0.005) parser.add_argument('--exploration-noise', type=float, default=0.1) parser.add_argument('--epoch', type=int, default=100) parser.add_argument('--step-per-epoch', type=int, default=2400) parser.add_argument('--collect-per-step', type=int, default=1) parser.add_argument('--batch-size', type=int, default=128) parser.add_argument('--layer-num', type=int, default=1) parser.add_argument('--training-num', type=int, default=1) parser.add_argument('--test-num', type=int, default=100) parser.add_argument('--logdir', type=str, default='log') parser.add_argument( '--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu') args = parser.parse_known_args()[0] return args def test_ddpg(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] # train_envs = gym.make(args.task) train_envs = VectorEnv( [lambda: gym.make(args.task) for _ in range(args.training_num)], reset_after_done=True) # test_envs = gym.make(args.task) test_envs = SubprocVectorEnv( [lambda: gym.make(args.task) for _ in range(args.test_num)], reset_after_done=False) # seed np.random.seed(args.seed) torch.manual_seed(args.seed) train_envs.seed(args.seed) test_envs.seed(args.seed) # model actor = Actor( args.layer_num, args.state_shape, args.action_shape, args.max_action, args.device ).to(args.device) actor_optim = torch.optim.Adam( actor.parameters(), lr=args.actor_lr, weight_decay=args.actor_wd) critic = Critic( args.layer_num, args.state_shape, args.action_shape, args.device ).to(args.device) critic_optim = torch.optim.Adam( critic.parameters(), lr=args.critic_lr, weight_decay=args.critic_wd) policy = DDPGPolicy( actor, actor_optim, critic, critic_optim, [env.action_space.low[0], env.action_space.high[0]], args.tau, args.gamma, args.exploration_noise) # collector train_collector = Collector( policy, train_envs, ReplayBuffer(args.buffer_size), 1) test_collector = Collector(policy, test_envs, stat_size=args.test_num) # log writer = SummaryWriter(args.logdir) def stop_fn(x): if args.task == 'Pendulum-v0': return x >= -250 else: return False # trainer train_step, train_episode, test_step, test_episode, best_rew, duration = \ step_trainer( policy, train_collector, test_collector, args.epoch, args.step_per_epoch, args.collect_per_step, args.test_num, args.batch_size, stop_fn=stop_fn, writer=writer) if args.task == 'Pendulum-v0': assert stop_fn(best_rew) train_collector.close() test_collector.close() if __name__ == '__main__': print(f'Collect {train_step} frame / {train_episode} episode during ' f'training and {test_step} frame / {test_episode} episode during' f' test in {duration:.2f}s, best_reward: {best_rew}, speed: ' f'{(train_step + test_step) / duration:.2f}it/s') # Let's watch its performance! env = gym.make(args.task) collector = Collector(policy, env) result = collector.collect(n_episode=1, render=1 / 35) print(f'Final reward: {result["rew"]}, length: {result["len"]}') collector.close() if __name__ == '__main__': test_ddpg()