写一个使用pytorch训练DQN的代码

我可以为您提供一个使用pytorch训练DQN的示例代码：import torch
import torch.nn as nn
import torch.optim as optimclass DQN(nn.Module):
def __init__(self, state_dim, action_dim):
super(DQN, self).__init__()
self.state_dim = state_dim
self.action_dim = action_dim
self.linear1 = nn.Linear(state_dim, 24)
self.relu1 = nn.ReLU()
self.linear2 = nn.Linear(24, 48)
self.relu2 = nn.ReLU()
self.linear3 = nn.Linear(48, action_dim)
self.optimizer = optim.Adam(self.parameters(), lr=0.001)
self.loss_func = nn.MSELoss() def forward(self, x):
out = self.linear1(x)
out = self.relu1(out)
out = self.linear2(out)
out = self.relu2(out)
out = self.linear3(out)
return out# 训练函数
def train(dqn, state, next_state, reward, done):
# 获取当前状态的Q值
q_value = dqn(state)
# 获取下一个状态的Q值
next_q_value = dqn(next_state)
# 计算期望值
expected_q_value = reward + gamma * torch.max(next_q_value) * (1 - done)
# 计算损失值
loss = dqn.loss_func(q_value, expected_q_value.detach())
# 更新参数
dqn.optimizer.zero_grad()
loss.backward()
dqn.optimizer.step()