dqn代码pytorch

DQN（Deep Q-Network）是一种使用深度学习网络对强化学习中的Q-learning算法进行改进的方法。代码实现可以使用PyTorch框架。下面是一个简单的示例代码：

首先，需要导入PyTorch库和其他必要的库，例如gym环境：

import torch
import torch.nn as nn
import torch.optim as optim
import gym

然后定义一个神经网络模型，用于近似Q函数。可以使用 nn.Module 类来创建模型。

class QNetwork(nn.Module):
    def __init__(self, state_size, action_size):
        super(QNetwork, self).__init__()
        self.fc1 = nn.Linear(state_size, 24)
        self.fc2 = nn.Linear(24, 24)
        self.fc3 = nn.Linear(24, action_size)

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

接下来，创建一个DQN对象，用于执行训练和测试：

class DQN:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.memory = ReplayMemory()  # Replay Memory用于存储训练数据
        self.q_network = QNetwork(state_size, action_size)  # 创建Q网络
        self.target_network = QNetwork(state_size, action_size)  # 创建目标网络
        self.target_network.load_state_dict(self.q_network.state_dict())
        self.optimizer = optim.Adam(self.q_network.parameters())
        self.criterion = nn.MSELoss()

    def train(self, batch_size):
        if len(self.memory) < batch_size:
            return
        transitions = self.memory.sample(batch_size)
        batch = Transition(*zip(*transitions))
        state_batch = torch.cat(batch.state)
        action_batch = torch.cat(batch.action)
        reward_batch = torch.cat(batch.reward)
        next_state_batch = torch.cat(batch.next_state)

        q_values = self.q_network(state_batch).gather(1, action_batch.unsqueeze(1))
        next_q_values = self.target_network(next_state_batch).detach().max(1)[0]
        expected_q_values = next_q_values * GAMMA + reward_batch

        loss = self.criterion(q_values, expected_q_values.unsqueeze(1))
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

    def update_target_network(self):
        self.target_network.load_state_dict(self.q_network.state_dict())

    def select_action(self, state, epsilon):
        if torch.rand(1)[0] > epsilon:
            with torch.no_grad():
                q_values = self.q_network(state)
                action = q_values.max(0)[1].view(1, 1)
        else:
            action = torch.tensor([[random.randrange(self.action_size)]], dtype=torch.long)
        return action

通过上述代码，可以定义一个DQN类，其中包括训练、更新目标网络、选择动作等功能。具体来说，train函数用于执行网络的训练过程，update_target_network函数用于更新目标网络的参数，select_action函数用于选择动作。

最后，可以使用gym环境进行训练和测试：

env = gym.make('CartPole-v1')
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
dqn = DQN(state_size, action_size)

for episode in range(EPISODES):
    state = env.reset()
    state = torch.tensor([state], dtype=torch.float32)
    done = False
    while not done:
        action = dqn.select_action(state, epsilon)
        next_state, reward, done, _ = env.step(action.item())
        next_state = torch.tensor([next_state], dtype=torch.float32)
        reward = torch.tensor([reward], dtype=torch.float32)
        dqn.memory.push(state, action, next_state, reward)
        state = next_state
        dqn.train(BATCH_SIZE)
        if episode % TARGET_UPDATE == 0:
            dqn.update_target_network()

这段代码旨在使用DQN算法对CartPole-v1环境进行训练。具体训练和测试的逻辑可以根据需要进行扩展和修改。希望以上内容对理解DQN的PyTorch实现有所帮助！