能帮我写一段TD3代码么

当然可以！以下是一个简单的TD3代
码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# 定义actor网络
class Actor(nn.Module):
    def __init__(self, state_dim, action_dim, max_action):
        super(Actor, self).__init__()
        self.layer1 = nn.Linear(state_dim, 400)
        self.layer2 = nn.Linear(400, 300)
        self.layer3 = nn.Linear(300, action_dim)
        self.max_action = max_action
        
    def forward(self, state):
        a = nn.functional.relu(self.layer1(state))
        a = nn.functional.relu(self.layer2(a))
        a = self.max_action * torch.tanh(self.layer3(a))
        return a

# 定义critic网络
class Critic(nn.Module):
    def __init__(self, state_dim, action_dim):
        super(Critic, self).__init__()
        # Q1架构
        self.layer1 = nn.Linear(state_dim + action_dim, 400)
        self.layer2 = nn.Linear(400, 300)
        self.layer3 = nn.Linear(300, 1)
        # Q2架构
        self.layer4 = nn.Linear(state_dim + action_dim, 400)
        self.layer5 = nn.Linear(400, 300)
        self.layer6 = nn.Linear(300, 1)
        
    def forward(self, state, action):
        sa = torch.cat([state, action], 1)
        q1 = nn.functional.relu(self.layer1(sa))
        q1 = nn.functional.relu(self.layer2(q1))
        q1 = self.layer3(q1)
        q2 = nn.functional.relu(self.layer4(sa))
        q2 = nn.functional.relu(self.layer5(q2))
        q2 = self.layer6(q2)
        return q1, q2

# 定义TD3算法
class TD3(object):
    def __init__(
        self,
        state_dim,
        action_dim,
        max_action
    ):
        self.actor = Actor(state_dim, action_dim, max_action).to(device)
        self.actor_target = Actor(state_dim, action_dim, max_action).to(device)
        self.actor_target.load_state_dict(self.actor.state_dict())
        self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=1e-3)
        
        self.critic = Critic(state_dim, action_dim).to(device)
        self.critic_target = Critic(state_dim, action_dim).to(device)
        self.critic_target.load_state_dict(self.critic.state_dict())
        self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=1e-3)
        
        self.max_action = max_action

    def select_action(self, state):
        state = torch.Tensor(state.reshape(1, -1)).to(device)
        return self.actor(state).cpu().data.numpy().flatten()

    def train(self, replay_buffer, iterations, batch_size=100, discount=0.99, tau=0.005, policy_noise=0.2, noise_clip=0.5, policy_freq=2):
        for it in range(iterations):
            # 从经验回放缓冲区中随机抽取一批数据
            batch_states, batch_next_states, batch_actions, batch_rewards, batch_dones = replay_buffer.sample(batch_size)
            state = torch.Tensor(batch_states).to(device)
            next_state = torch.Tensor(batch_next_states).to(device)
            action = torch.Tensor(batch_actions).to(device)
            reward = torch.Tensor