多智能体 分布式规划实现代码

多智能体分布式规划的实现代码可以采用一些著名的框架，例如MADDPG和MARL。以下是一个使用MADDPG算法实现的多智能体分布式规划的示例代码：

import numpy as np
import torch
import torch.nn.functional as F
import torch.optim as optim
from model import Actor, Critic
from memory import ReplayBuffer

class MADDPG:
    def __init__(self, state_dim, action_dim, num_agents, actor_hidden_dim, critic_hidden_dim,
                 actor_lr, critic_lr, gamma, tau, buffer_size, batch_size):
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.num_agents = num_agents
        self.actor_hidden_dim = actor_hidden_dim
        self.critic_hidden_dim = critic_hidden_dim
        self.actor_lr = actor_lr
        self.critic_lr = critic_lr
        self.gamma = gamma
        self.tau = tau
        self.buffer_size = buffer_size
        self.batch_size = batch_size
        
        self.actor_local = [Actor(state_dim, action_dim, actor_hidden_dim) for i in range(num_agents)]
        self.actor_target = [Actor(state_dim, action_dim, actor_hidden_dim) for i in range(num_agents)]
        self.critic_local = Critic(state_dim * num_agents, action_dim * num_agents, critic_hidden_dim)
        self.critic_target = Critic(state_dim * num_agents, action_dim * num_agents, critic_hidden_dim)
        self.memory = ReplayBuffer(buffer_size, batch_size)
        self.actor_optimizers = [optim.Adam(actor.parameters(), lr=actor_lr) for actor in self.actor_local]
        self.critic_optimizer = optim.Adam(self.critic_local.parameters(), lr=critic_lr)
        
    def act(self, obs, noise=0.0):
        actions = []
        for i in range(self.num_agents):
            obs = torch.from_numpy(obs[i]).float().unsqueeze(0)
            action = self.actor_local[i](obs).squeeze(0).detach().numpy()
            action += noise * np.random.normal(size=self.action_dim)
            action = np.clip(action, -1, 1)
            actions.append(action)
        return actions
    
    def step(self, state, action, reward, next_state, done):
        self.memory.add(state, action, reward, next_state, done)
        if len(self.memory) > self.batch_size:
            experiences = self.memory.sample()
            self.learn(experiences)
            
    def learn(self, experiences):
        states, actions, rewards, next_states, dones = experiences
        
        # update critic
        with torch.no_grad():
            actions_next = [self.actor_target[i](next_states[:,i,:]) for i in range(self.num_agents)]
            actions_next = torch.cat(actions_next, dim=1)
            q_targets_next = self.critic_target(next_states.view(-1, self.state_dim * self.num_agents), actions_next)
            q_targets = rewards.sum(dim=1, keepdim=True) + self.gamma * q_targets_next * (1 - dones.sum(dim=1, keepdim=True))
        q_expected = self.critic_local(states.view(-1, self.state_dim * self.num_agents), actions.view(-1, self.action_dim * self.num_agents))
        critic_loss = F.mse_loss(q_expected, q_targets)
        self.critic_optimizer.zero_grad()
        critic_loss.backward()
        self.critic_optimizer.step()
        
        # update actor
        for i in range(self.num_agents):
            actions_i = actions.clone()
            actions_i[:,i,:] = self.actor_local[i](states[:,i,:])
            actor_loss = -self.critic_local(states.view(-1, self.state_dim * self.num_agents), actions_i.view(-1, self.action_dim * self.num_agents)).mean()
            self.actor_optimizers[i].zero_grad()
            actor_loss.backward()
            self.actor_optimizers[i].step()
        
        # update target networks
        self.soft_update(self.critic_local, self.critic_target)
        for i in range(self.num_agents):
            self.soft_update(self.actor_local[i], self.actor_target[i])
            
    def soft_update(self, local_model, target_model):
        for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
            target_param.data.copy_(self.tau*local_param.data + (1.0-self.tau)*target_param.data)

上述代码中的`Actor`和`Critic`模型可以使用PyTorch定义，`ReplayBuffer`可以使用一个简单的数组或者更为高效的数据结构，例如优先经验回放。

此外，还需要编写一个主循环，以与环境交互并调用`MADDPG`实例的`act`和`step`方法，例如：

while True:
    obs = env.reset()
    noise = max(0.1, 1.0 - episode / 1000.0) * 0.2
    for t in range(max_steps):
        actions = agent.act(obs, noise)
        next_obs, rewards, dones, _ = env.step(actions)
        agent.step(obs, actions, rewards, next_obs, dones)
        obs = next_obs
        if np.any(dones):
            break
    episode += 1

其中，`env`是与环境交互的对象，`max_steps`是每个episode的最大步数，`episode`是当前episode的编号，`noise`是加到动作上的高斯噪声的标准差，随着训练进行而减小。