用pytorch写一个深度强化学习代码

好的，以下是一个用PyTorch实现的深度强化学习代码，它使用DQN算法来训练一个智能体在CartPole游戏中保持平衡。

import gym
import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F

# 定义DQN网络
class DQN(nn.Module):
    def __init__(self, input_size, output_size, hidden_size=128):
        super(DQN, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 定义经验回放缓存
class ReplayBuffer():
    def __init__(self, capacity):
        self.capacity = capacity
        self.buffer = []
        self.position = 0

    def push(self, state, action, reward, next_state, done):
        if len(self.buffer) < self.capacity:
            self.buffer.append(None)
        self.buffer[self.position] = (state, action, reward, next_state, done)
        self.position = (self.position + 1) % self.capacity

    def sample(self, batch_size):
        batch = random.sample(self.buffer, batch_size)
        state, action, reward, next_state, done = map(np.stack, zip(*batch))
        return state, action.squeeze(), reward.squeeze(), next_state, done.squeeze()

    def __len__(self):
        return len(self.buffer)

# 定义DQN智能体
class DQNAgent():
    def __init__(self, state_size, action_size, memory_capacity=10000, batch_size=64, gamma=0.99, epsilon_start=1.0, epsilon_end=0.01, epsilon_decay=0.995):
        self.state_size = state_size
        self.action_size = action_size
        self.memory = ReplayBuffer(memory_capacity)
        self.batch_size = batch_size
        self.gamma = gamma
        self.epsilon_start = epsilon_start
        self.epsilon_end = epsilon_end
        self.epsilon_decay = epsilon_decay
        self.epsilon = epsilon_start
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.model = DQN(state_size, action_size).to(self.device)
        self.target_model = DQN(state_size, action_size).to(self.device)
        self.optimizer = optim.Adam(self.model.parameters())
        self.update_target_model()

    def update_target_model(self):
        self.target_model.load_state_dict(self.model.state_dict())

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return np.random.choice(self.action_size)
        state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
        with torch.no_grad():
            values = self.model(state)
        return np.argmax(values.cpu().data.numpy())

    def remember(self, state, action, reward, next_state, done):
        self.memory.push(state, action, reward, next_state, done)

    def replay(self):
        if len(self.memory) < self.batch_size:
            return
        state, action, reward, next_state, done = self.memory.sample(self.batch_size)
        state = torch.FloatTensor(state).to(self.device)
        action = torch.LongTensor(action).unsqueeze(1).to(self.device)
        reward = torch.FloatTensor(reward).unsqueeze(1).to(self.device)
        next_state = torch.FloatTensor(next_state).to(self.device)
        done = torch.FloatTensor(done).unsqueeze(1).to(self.device)
        q_values = self.model(state).gather(1, action)
        next_q_values = self.target_model(next_state).detach().max(1)[0].unsqueeze(1)
        expected_q_values = reward + self.gamma * next_q_values * (1 - done)
        loss = F.mse_loss(q_values, expected_q_values)
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

    def decay_epsilon(self):
        self.epsilon = max(self.epsilon * self.epsilon_decay, self.epsilon_end)

# 初始化环境和智能体
env = gym.make('CartPole-v0')
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
agent = DQNAgent(state_size, action_size)

# 训练智能体
num_episodes = 1000
for episode in range(num_episodes):
    state = env.reset()
    done = False
    total_reward = 0
    while not done:
        action = agent.act(state)
        next_state, reward, done, _ = env.step(action)
        agent.remember(state, action, reward, next_state, done)
        total_reward += reward
        state = next_state
        agent.replay()
        agent.decay_epsilon()
        agent.update_target_model()
    print('Episode: {}, Total Reward: {}'.format(episode, total_reward))

# 测试智能体
state = env.reset()
done = False
total_reward = 0
while not done:
    action = agent.act(state)
    state, reward, done, _ = env.step(action)
    total_reward += reward
    env.render()
print('Total Reward: {}'.format(total_reward))

env.close()

这个代码使用DQN算法来训练一个智能体在CartPole游戏中保持平衡，其中包括定义DQN网络、经验回放缓存、DQN智能体等类，以及训练和测试智能体的代码。你可以通过修改超参数和环境来探索不同的深度强化学习应用。