diff --git a/test/continuous/net.py b/test/continuous/net.py index 2b12420..f5184f0 100644 --- a/test/continuous/net.py +++ b/test/continuous/net.py @@ -26,8 +26,33 @@ class Actor(nn.Module): return logits, None +class ActorProb(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.Sequential(*self.model) + self.mu = nn.Linear(128, np.prod(action_shape)) + self.sigma = nn.Linear(128, np.prod(action_shape)) + 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) + mu = self._max * torch.tanh(self.mu(logits)) + sigma = torch.exp(self.sigma(logits)) + return (mu, sigma), None + + class Critic(nn.Module): - def __init__(self, layer_num, state_shape, action_shape, device='cpu'): + def __init__(self, layer_num, state_shape, action_shape=0, device='cpu'): super().__init__() self.device = device self.model = [ @@ -38,12 +63,15 @@ class Critic(nn.Module): self.model += [nn.Linear(128, 1)] self.model = nn.Sequential(*self.model) - def forward(self, s, a): + def forward(self, s, a=None): 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)) + if a is None: + logits = self.model(s) + else: + a = a.view(batch, -1) + logits = self.model(torch.cat([s, a], dim=1)) return logits diff --git a/test/continuous/test_ppo.py b/test/continuous/test_ppo.py new file mode 100644 index 0000000..abef40b --- /dev/null +++ b/test/continuous/test_ppo.py @@ -0,0 +1,116 @@ +import gym +import torch +import pprint +import argparse +import numpy as np +from torch.utils.tensorboard import SummaryWriter + +from tianshou.policy import PPOPolicy +from tianshou.env import SubprocVectorEnv +from tianshou.trainer import onpolicy_trainer +from tianshou.data import Collector, ReplayBuffer + +if __name__ == '__main__': + from net import ActorProb, Critic +else: # pytest + from test.continuous.net import ActorProb, Critic + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument('--task', type=str, default='Pendulum-v0') + parser.add_argument('--seed', type=int, default=0) + parser.add_argument('--buffer-size', type=int, default=20000) + parser.add_argument('--lr', type=float, default=1e-3) + parser.add_argument('--gamma', type=float, default=0.9) + parser.add_argument('--epoch', type=int, default=100) + parser.add_argument('--step-per-epoch', type=int, default=1000) + parser.add_argument('--collect-per-step', type=int, default=10) + parser.add_argument('--repeat-per-collect', type=int, default=1) + parser.add_argument('--batch-size', type=int, default=64) + parser.add_argument('--layer-num', type=int, default=1) + parser.add_argument('--training-num', type=int, default=16) + 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') + # ppo special + parser.add_argument('--vf-coef', type=float, default=0.5) + parser.add_argument('--ent-coef', type=float, default=0.0) + parser.add_argument('--eps-clip', type=float, default=0.2) + parser.add_argument('--max-grad-norm', type=float, default=0.5) + args = parser.parse_known_args()[0] + return args + + +def _test_ppo(args=get_args()): + # just a demo, I have not made it work :( + env = gym.make(args.task) + if args.task == 'Pendulum-v0': + env.spec.reward_threshold = -250 + 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 = SubprocVectorEnv( + [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 = ActorProb( + args.layer_num, args.state_shape, args.action_shape, + args.max_action, args.device + ).to(args.device) + critic = Critic( + args.layer_num, args.state_shape, device=args.device + ).to(args.device) + optim = torch.optim.Adam(list( + actor.parameters()) + list(critic.parameters()), lr=args.lr) + dist = torch.distributions.Normal + policy = PPOPolicy( + actor, critic, optim, dist, args.gamma, + max_grad_norm=args.max_grad_norm, + eps_clip=args.eps_clip, + vf_coef=args.vf_coef, + ent_coef=args.ent_coef, + action_range=[env.action_space.low[0], env.action_space.high[0]]) + # collector + train_collector = Collector( + policy, train_envs, ReplayBuffer(args.buffer_size)) + test_collector = Collector(policy, test_envs, stat_size=args.test_num) + train_collector.collect(n_step=args.step_per_epoch) + # log + writer = SummaryWriter(args.logdir) + + def stop_fn(x): + return x >= env.spec.reward_threshold + + # trainer + result = onpolicy_trainer( + policy, train_collector, test_collector, args.epoch, + args.step_per_epoch, args.collect_per_step, args.repeat_per_collect, + args.test_num, args.batch_size, stop_fn=stop_fn, writer=writer) + assert stop_fn(result['best_reward']) + train_collector.close() + test_collector.close() + if __name__ == '__main__': + pprint.pprint(result) + # 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_ppo()