用Pytorch实现DQN的代码

在PyTorch中实现Deep Q-Network (DQN) 需要几个关键组件：神经网络模型、经验回放机制、目标网络和Q-learning更新步骤。这里是一个简单的DQN实现框架：

import torch
import torch.nn as nn
from torch.optim import Adam
import random

class DQN(nn.Module):
    def __init__(self, state_size, action_size):
        super(DQN, self).__init__()
        self.fc1 = nn.Linear(state_size, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, action_size)

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

# 初始化DQN模型
model = DQN(state_size, action_size)
target_model = DQN(state_size, action_size)  # 目标网络，定期复制当前模型参数
optimizer = Adam(model.parameters())
memory = []  # 回忆存储

def train_step(state, action, reward, next_state, done):
    # 添加记忆到经验回放缓冲区
    memory.append((state, action, reward, next_state, done))

    if len(memory) > BATCH_SIZE:  # 当缓存足够大时开始训练
        experiences = random.sample(memory, BATCH_SIZE)
        states, actions, rewards, next_states, dones = zip(*experiences)

        # 计算Q值和下一个状态的Q值估计（使用目标网络）
        q_values = model(states).gather(1, actions.unsqueeze(1)).squeeze()  # 现在的Q值
        target_q_values = target_model(next_states).max(1)[0].unsqueeze(1) * GAMMA * (~dones).float() + rewards  # 标准化的目标Q值

        # 梯度下降更新
        loss = F.smooth_l1_loss(q_values, target_q_values)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

if __name__ == "__main__":
    for episode in range(NUM_EPISODES):
        # ... 进行游戏环境交互，执行动作等
        train_step(...)

    # 定期更新目标网络参数
    if episode % TARGET_UPDATE_FREQ == 0:
        target_model.load_state_dict(model.state_dict())

在这个示例中，`BATCH_SIZE`是经验回放中的样本大小，`GAMMA`是折扣因子，`NUM_EPISODES`是总的训练回合数，`TARGET_UPDATE_FREQ`是目标网络参数同步频率。