pytorch DQN代码实例

当然，我可以给你一个基于PyTorch的DQN（Deep Q-Network）代码实例。以下是一个简单的示例：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import random

class DQN(nn.Module):
    def __init__(self, state_size, action_size):
        super(DQN, self).__init__()
        self.fc1 = nn.Linear(state_size, 64)
        self.fc2 = nn.Linear(64, 64)
        self.fc3 = nn.Linear(64, action_size)
        
    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

class ReplayBuffer:
    def __init__(self, capacity):
        self.capacity = capacity
        self.buffer = []
        
    def push(self, experience):
        if len(self.buffer) >= self.capacity:
            self.buffer.pop(0)
        self.buffer.append(experience)
        
    def sample(self, batch_size):
        return random.sample(self.buffer, batch_size)

class DQNAgent:
    def __init__(self, state_size, action_size, replay_buffer_capacity):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.state_size = state_size
        self.action_size = action_size
        self.replay_buffer = ReplayBuffer(replay_buffer_capacity)
        
        self.model = DQN(state_size, action_size).to(self.device)
        self.target_model = DQN(state_size, action_size).to(self.device)
        self.target_model.load_state_dict(self.model.state_dict())
        
        self.optimizer = optim.Adam(self.model.parameters(), lr=0.001)
        
    def act(self, state, epsilon):
        if random.random() > epsilon:
            state = torch.tensor(state, dtype=torch.float32).unsqueeze(0).to(self.device)
            with torch.no_grad():
                action_values = self.model(state)
            action = torch.argmax(action_values).item()
        else:
            action = random.randint(0, self.action_size - 1)
        return action
    
    def update_target_model(self):
        self.target_model.load_state_dict(self.model.state_dict())
        
    def train(self, batch_size, gamma):
        if len(self.replay_buffer.buffer) < batch_size:
            return
        
        transitions = self.replay_buffer.sample(batch_size)
        batch = list(zip(*transitions))
        
        states = torch.tensor(batch[0], dtype=torch.float32).to(self.device)
        actions = torch.tensor(batch[1], dtype=torch.long).to(self.device)
        rewards = torch.tensor(batch[2], dtype=torch.float32).to(self.device)
        next_states = torch.tensor(batch[3], dtype=torch.float32).to(self.device)
        dones = torch.tensor(batch[4], dtype=torch.float32).to(self.device)
        
        q_values = self.model(states).gather(1, actions.unsqueeze(1)).squeeze(1)
        next_q_values = self.target_model(next_states).max(1)[0].detach()
        expected_q_values = rewards + gamma * next_q_values * (1 - dones)
        
        loss = F.smooth_l1_loss(q_values, expected_q_values)
        
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

在这个示例中，我们定义了一个 `DQN` 类作为我们的深度Q网络，它包含三个全连接层。`ReplayBuffer` 类用于存储和采样经验回放缓冲区。`DQNAgent` 类则是我们的智能体，包含了模型、目标模型、优化器以及一些用于训练和动作选择的方法。

请注意，这只是一个简单的示例，你可能需要根据你的问题进行修改和扩展。你还需要实现环境来与智能体进行交互，收集经验并调用 `train` 方法进行训练。

希望这个代码示例能对你有所帮助！