TD3的Python代码

TD3 ( Twin Delayed Deep Deterministic Policy Gradients) 是一种改进版的深度强化学习算法，它通过延迟策略更新和目标网络的更新来提高性能。以下是 TD3 的基本 Python 代码框架示例：

import torch
from torch import nn

class Actor(nn.Module):
    # ... (Actor网络结构定义)

class Critic(nn.Module):
    # ... (Critic网络结构定义)

class TD3Agent:
    def __init__(self, actor, critic_1, critic_2, tau=0.005, gamma=0.99):
        self.actor = actor
        self.critic_1 = critic_1
        self.critic_2 = critic_2
        self.tau = tau
        self.gamma = gamma
        self.target_actor = actor.clone()  # 延迟更新的目标actor
        self.target_critic_1 = critic_1.clone()
        self.target_critic_2 = critic_2.clone()

    # ... (其他函数如选择动作、更新步骤等)

def update_agent(obs, action, reward, next_obs, done):
        # 更新critics
        with torch.no_grad():
            target_action = self.target_actor(next_obs)
            target_q_values = min(self.target_critic_1(next_obs, target_action),
                                 self.target_critic_2(next_obs, target_action))
            expected_q_value = reward + (1 - done) * self.gamma * target_q_values

        current_q_values = self.critic_1(obs, action)
        critic_loss = F.mse_loss(current_q_values, expected_q_value.unsqueeze(1))

        # 更新actor
        policy_gradient = self.actor(obs).detach().mean(dim=0)
        actor_loss = -self.critic_1(obs, policy_gradient).mean()

        # 更新target networks
        for param, target_param in zip(self.critic_1.parameters(), self.target_critic_1.parameters()):
            target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)

        for param, target_param in zip(self.critic_2.parameters(), self.target_critic_2.parameters()):
            target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)

        # 打印损失并优化
        agent.optimizer.zero_grad()
        (actor_loss + critic_loss).backward()
        agent.optimizer.step()

# 使用示例
agent = TD3Agent(actor, critic_1, critic_2)
obs = env.reset()
while True:
    action = agent.select_action(obs)
    obs, reward, done, _ = env.step(action)
    update_agent(obs, action, reward, obs, done)

请注意，这只是一个简化的例子，实际应用中还需要处理device迁移、添加噪声、经验回放缓冲区以及其他的训练细节。