121 lines
4.3 KiB
Python
121 lines
4.3 KiB
Python
import gym
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import torch
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import pprint
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import argparse
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import numpy as np
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from torch import nn
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.policy import DQNPolicy
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from tianshou.env import SubprocVectorEnv
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from tianshou.trainer import offpolicy_trainer
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from tianshou.data import Collector, ReplayBuffer
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class Net(nn.Module):
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def __init__(self, layer_num, state_shape, action_shape, device='cpu'):
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super().__init__()
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self.device = device
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self.model = [
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nn.Linear(np.prod(state_shape), 128),
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nn.ReLU(inplace=True)]
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for i in range(layer_num):
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self.model += [nn.Linear(128, 128), nn.ReLU(inplace=True)]
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self.model += [nn.Linear(128, np.prod(action_shape))]
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self.model = nn.Sequential(*self.model)
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def forward(self, s, **kwargs):
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s = torch.tensor(s, device=self.device, dtype=torch.float)
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batch = s.shape[0]
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q = self.model(s.view(batch, -1))
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return q, None
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--task', type=str, default='CartPole-v0')
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parser.add_argument('--seed', type=int, default=1626)
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parser.add_argument('--eps-test', type=float, default=0.05)
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parser.add_argument('--eps-train', type=float, default=0.1)
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parser.add_argument('--buffer-size', type=int, default=20000)
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parser.add_argument('--lr', type=float, default=3e-4)
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parser.add_argument('--gamma', type=float, default=0.9)
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parser.add_argument('--n-step', type=int, default=1)
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parser.add_argument('--epoch', type=int, default=100)
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parser.add_argument('--step-per-epoch', type=int, default=320)
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parser.add_argument('--collect-per-step', type=int, default=10)
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parser.add_argument('--batch-size', type=int, default=64)
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parser.add_argument('--layer-num', type=int, default=3)
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parser.add_argument('--training-num', type=int, default=8)
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parser.add_argument('--test-num', type=int, default=100)
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parser.add_argument('--logdir', type=str, default='log')
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parser.add_argument(
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'--device', type=str,
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default='cuda' if torch.cuda.is_available() else 'cpu')
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args = parser.parse_known_args()[0]
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return args
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def test_dqn(args=get_args()):
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env = gym.make(args.task)
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args.state_shape = env.observation_space.shape or env.observation_space.n
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args.action_shape = env.action_space.shape or env.action_space.n
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# train_envs = gym.make(args.task)
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train_envs = SubprocVectorEnv(
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[lambda: gym.make(args.task) for _ in range(args.training_num)],
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reset_after_done=True)
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# test_envs = gym.make(args.task)
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test_envs = SubprocVectorEnv(
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[lambda: gym.make(args.task) for _ in range(args.test_num)],
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reset_after_done=False)
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# seed
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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train_envs.seed(args.seed)
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test_envs.seed(args.seed)
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# model
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net = Net(args.layer_num, args.state_shape, args.action_shape, args.device)
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net = net.to(args.device)
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optim = torch.optim.Adam(net.parameters(), lr=args.lr)
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policy = DQNPolicy(net, optim, args.gamma, args.n_step)
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# collector
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train_collector = Collector(
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policy, train_envs, ReplayBuffer(args.buffer_size))
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test_collector = Collector(policy, test_envs, stat_size=args.test_num)
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train_collector.collect(n_step=args.batch_size)
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# log
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writer = SummaryWriter(args.logdir)
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def stop_fn(x):
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return x >= env.spec.reward_threshold
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def train_fn(x):
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policy.sync_weight()
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policy.set_eps(args.eps_train)
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def test_fn(x):
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policy.set_eps(args.eps_test)
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# trainer
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result = offpolicy_trainer(
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policy, train_collector, test_collector, args.epoch,
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args.step_per_epoch, args.collect_per_step, args.test_num,
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args.batch_size, train_fn=train_fn, test_fn=test_fn,
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stop_fn=stop_fn, writer=writer)
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assert stop_fn(result['best_reward'])
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train_collector.close()
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test_collector.close()
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if __name__ == '__main__':
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pprint.pprint(result)
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# Let's watch its performance!
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env = gym.make(args.task)
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collector = Collector(policy, env)
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result = collector.collect(n_episode=1, render=1 / 35)
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print(f'Final reward: {result["rew"]}, length: {result["len"]}')
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collector.close()
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if __name__ == '__main__':
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test_dqn(get_args())
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