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下面是一个使用Python实现基于TD3（Twin Delayed Deep Deterministic Policy Gradient）算法来实时更新路径规划算法的三个参数（sigma0，rho0 和 theta）的示例代码。该算法将依据障碍物环境进行优化。

实现思路

1. 环境定义：定义一个包含障碍物的环境，用于模拟路径规划问题。
2. TD3算法：使用TD3算法来学习如何优化路径规划算法的三个参数。
3. 训练过程：在环境中进行训练，不断更新策略网络和价值网络。

代码示例

import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import random
from collections import deque# 定义TD3网络
class Actor(nn.Module):def __init__(self, state_dim, action_dim, max_action):super(Actor, self).__init__()self.fc1 = nn.Linear(state_dim, 400)self.fc2 = nn.Linear(400, 300)self.fc3 = nn.Linear(300, action_dim)self.max_action = max_actiondef forward(self, state):x = torch.relu(self.fc1(state))x = torch.relu(self.fc2(x))x = self.max_action * torch.tanh(self.fc3(x))return xclass Critic(nn.Module):def __init__(self, state_dim, action_dim):super(Critic, self).__init__()# Q1架构self.fc1 = nn.Linear(state_dim + action_dim, 400)self.fc2 = nn.Linear(400, 300)self.fc3 = nn.Linear(300, 1)# Q2架构self.fc4 = nn.Linear(state_dim + action_dim, 400)self.fc5 = nn.Linear(400, 300)self.fc6 = nn.Linear(300, 1)def forward(self, state, action):sa = torch.cat([state, action], 1)# Q1q1 = torch.relu(self.fc1(sa))q1 = torch.relu(self.fc2(q1))q1 = self.fc3(q1)# Q2q2 = torch.relu(self.fc4(sa))q2 = torch.relu(self.fc5(q2))q2 = self.fc6(q2)return q1, q2def Q1(self, state, action):sa = torch.cat([state, action], 1)q1 = torch.relu(self.fc1(sa))q1 = torch.relu(self.fc2(q1))q1 = self.fc3(q1)return q1# TD3算法类
class TD3:def __init__(self, state_dim, action_dim, max_action):self.actor = Actor(state_dim, action_dim, max_action)self.actor_target = Actor(state_dim, action_dim, max_action)self.actor_target.load_state_dict(self.actor.state_dict())self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=3e-4)self.critic = Critic(state_dim, action_dim)self.critic_target = Critic(state_dim, action_dim)self.critic_target.load_state_dict(self.critic.state_dict())self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=3e-4)self.max_action = max_actionself.gamma = 0.99self.tau = 0.005self.policy_noise = 0.2self.noise_clip = 0.5self.policy_freq = 2self.total_it = 0def select_action(self, state):state = torch.FloatTensor(state.reshape(1, -1))return self.actor(state).cpu().data.numpy().flatten()def train(self, replay_buffer, batch_size=100):self.total_it += 1# 从回放缓冲区采样state, action, next_state, reward, not_done = replay_buffer.sample(batch_size)with torch.no_grad():# 选择动作并添加噪声noise = (torch.randn_like(action) * self.policy_noise).clamp(-self.noise_clip, self.noise_clip)next_action = (self.actor_target(next_state) + noise).clamp(-self.max_action, self.max_action)# 计算目标Q值target_Q1, target_Q2 = self.critic_target(next_state, next_action)target_Q = torch.min(target_Q1, target_Q2)target_Q = reward + not_done * self.gamma * target_Q# 获取当前Q估计值current_Q1, current_Q2 = self.critic(state, action)# 计算批评损失critic_loss = nn.MSELoss()(current_Q1, target_Q) + nn.MSELoss()(current_Q2, target_Q)# 优化批评网络self.critic_optimizer.zero_grad()critic_loss.backward()self.critic_optimizer.step()# 延迟策略更新if self.total_it % self.policy_freq == 0:# 计算演员损失actor_loss = -self.critic.Q1(state, self.actor(state)).mean()# 优化演员网络self.actor_optimizer.zero_grad()actor_loss.backward()self.actor_optimizer.step()# 软更新目标网络for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)# 回放缓冲区类
class ReplayBuffer:def __init__(self, max_size):self.buffer = deque(maxlen=max_size)def add(self, state, action, next_state, reward, done):self.buffer.append((state, action, next_state, reward, 1 - done))def sample(self, batch_size):state, action, next_state, reward, not_done = zip(*random.sample(self.buffer, batch_size))return torch.FloatTensor(state), torch.FloatTensor(action), torch.FloatTensor(next_state), torch.FloatTensor(reward).unsqueeze(1), torch.FloatTensor(not_done).unsqueeze(1)def __len__(self):return len(self.buffer)# 模拟路径规划环境
class PathPlanningEnv:def __init__(self):# 简单模拟障碍物环境，这里用一个二维数组表示self.obstacles = np.random.randint(0, 2, (10, 10))self.state_dim = 10 * 10  # 环境状态维度self.action_dim = 3  # 三个参数 sigma0, rho0, thetaself.max_action = 1.0def reset(self):# 重置环境return self.obstacles.flatten()def step(self, action):sigma0, rho0, theta = action# 简单模拟奖励计算，这里可以根据实际路径规划算法修改reward = np.random.randn()done = Falsenext_state = self.obstacles.flatten()return next_state, reward, done# 主训练循环
def main():env = PathPlanningEnv()state_dim = env.state_dimaction_dim = env.action_dimmax_action = env.max_actiontd3 = TD3(state_dim, action_dim, max_action)replay_buffer = ReplayBuffer(max_size=1000000)total_steps = 10000episode_steps = 0state = env.reset()for step in range(total_steps):episode_steps += 1# 选择动作action = td3.select_action(state)# 执行动作next_state, reward, done = env.step(action)# 将经验添加到回放缓冲区replay_buffer.add(state, action, next_state, reward, done)# 训练TD3if len(replay_buffer) > 100:td3.train(replay_buffer)state = next_stateif done or episode_steps >= 100:state = env.reset()episode_steps = 0# 输出最终优化的参数final_state = env.reset()final_action = td3.select_action(final_state)sigma0, rho0, theta = final_actionprint(f"Optimized sigma0: {sigma0}, rho0: {rho0}, theta: {theta}")if __name__ == "__main__":main()

代码解释

1. 网络定义：定义了 Actor 和 Critic 网络，分别用于生成动作和评估动作的价值。
2. TD3类：实现了TD3算法的核心逻辑，包括动作选择、训练和目标网络的软更新。
3. ReplayBuffer类：用于存储和采样经验数据。
4. PathPlanningEnv类：模拟了一个包含障碍物的路径规划环境，提供了重置和执行动作的方法。
5. 主训练循环：在环境中进行训练，不断更新策略网络和价值网络。

注意事项

• 此示例中的奖励计算是简单模拟的，实际应用中需要根据具体的路径规划算法进行修改。
• 障碍物环境的表示可以根据实际需求进行调整。