AI人工智能领域多智能体系统：开启智能协作新篇章_副本

 * 应用示例：多机器人协同搬运
 * 技术挑战：信用分配、协调策略

 * 应用示例：博弈游戏AI
 * 技术挑战：纳什均衡计算、对手建模

 * 应用示例：经济市场模拟
 * 技术挑战：联盟形成、谈判协议

 * 协议示例：FIPA ACL
 * 优点：精确控制信息交换
 * 缺点：通信开销大

 * 示例：信息素标记(蚁群算法)
 * 优点：扩展性好
 * 缺点：信息不精确

 * 示例：人类社交网络
 * 优点：灵活性强
 * 缺点：实现复杂

    import torch
    import torch.nn as nn
    import torch.optim as optim
    import numpy as np
    from collections import deque, namedtuple
    import random
    
    # 定义经验回放缓冲区
    Experience = namedtuple('Experience', ['state', 'action', 'reward', 'next_state', 'done'])
    class ReplayBuffer:
    def __init__(self, capacity):
        self.buffer = deque(maxlen=capacity)
    
    def push(self, *args):
        self.buffer.append(Experience(*args))
    
    def sample(self, batch_size):
        return random.sample(self.buffer, batch_size)
    
    def __len__(self):
        return len(self.buffer)
    
    # 定义Actor网络
    class Actor(nn.Module):
    def __init__(self, state_dim, action_dim, hidden_dim=256):
        super(Actor, self).__init__()
        self.fc1 = nn.Linear(state_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, hidden_dim)
        self.fc3 = nn.Linear(hidden_dim, action_dim)
        self.activation = nn.ReLU()
        self.tanh = nn.Tanh()
    
    def forward(self, x):
        x = self.activation(self.fc1(x))
        x = self.activation(self.fc2(x))
        return self.tanh(self.fc3(x))
    
    # 定义Critic网络
    class Critic(nn.Module):
    def __init__(self, state_dim, action_dim, hidden_dim=256):
        super(Critic, self).__init__()
        self.fc1 = nn.Linear(state_dim + action_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, hidden_dim)
        self.fc3 = nn.Linear(hidden_dim, 1)
        self.activation = nn.ReLU()
    
    def forward(self, state, action):
        x = torch.cat([state, action], dim=1)
        x = self.activation(self.fc1(x))
        x = self.activation(self.fc2(x))
        return self.fc3(x)
    
    # MADDPG算法主体
    class MADDPG:
    def __init__(self, n_agents, state_dims, action_dims, gamma=0.99, tau=0.01, lr_actor=1e-4, lr_critic=1e-3):
        self.n_agents = n_agents
        self.gamma = gamma
        self.tau = tau
    
        # 为每个智能体创建Actor和Critic网络
        self.actors = [Actor(state_dims[i], action_dims[i]) for i in range(n_agents)]
        self.critics = [Critic(sum(state_dims), sum(action_dims)) for _ in range(n_agents)]
    
        # 目标网络
        self.target_actors = [Actor(state_dims[i], action_dims[i]) for i in range(n_agents)]
        self.target_critics = [Critic(sum(state_dims), sum(action_dims)) for _ in range(n_agents)]
    
        # 初始化目标网络参数
        for i in range(n_agents):
            self.target_actors[i].load_state_dict(self.actors[i].state_dict())
            self.target_critics[i].load_state_dict(self.critics[i].state_dict())
    
        # 优化器
        self.actor_optimizers = [optim.Adam(self.actors[i].parameters(), lr=lr_actor) for i in range(n_agents)]
        self.critic_optimizers = [optim.Adam(self.critics[i].parameters(), lr=lr_critic) for i in range(n_agents)]
    
        # 经验回放
        self.buffer = ReplayBuffer(100000)
    
    def select_action(self, agent_idx, state, noise_scale=0.1):
        state = torch.FloatTensor(state).unsqueeze(0)
        action = self.actors[agent_idx](state).squeeze(0).detach().numpy()
        noise = noise_scale * np.random.randn(len(action))
        return np.clip(action + noise, -1, 1)
    
    def update(self, batch_size):
        if len(self.buffer) < batch_size:
            return
    
        # 从缓冲区采样
        batch = self.buffer.sample(batch_size)
        batch = Experience(*zip(*batch))
    
        # 转换为张量
        states = torch.FloatTensor(np.array(batch.state))
        actions = torch.FloatTensor(np.array(batch.action))
        rewards = torch.FloatTensor(np.array(batch.reward)).unsqueeze(1)
        next_states = torch.FloatTensor(np.array(batch.next_state))
        dones = torch.FloatTensor(np.array(batch.done)).unsqueeze(1)
    
        # 更新每个智能体的Critic和Actor
        for i in range(self.n_agents):
            # Critic更新
            with torch.no_grad():
                # 计算目标Q值
                next_actions = torch.cat([self.target_actors[j](next_states[:, j]) for j in range(self.n_agents)], dim=1)
                target_Q = self.target_critics[i](next_states.view(batch_size, -1), next_actions)
                target_Q = rewards[:, i] + (1 - dones[:, i]) * self.gamma * target_Q
    
            # 当前Q值
            current_Q = self.critics[i](states.view(batch_size, -1), actions.view(batch_size, -1))
    
            # Critic损失
            critic_loss = nn.MSELoss()(current_Q, target_Q.detach())
    
            # 优化Critic
            self.critic_optimizers[i].zero_grad()
            critic_loss.backward()
            self.critic_optimizers[i].step()
    
            # Actor更新
            # 重新计算动作，保持其他智能体动作不变
            new_actions = []
            for j in range(self.n_agents):
                if j == i:
                    new_actions.append(self.actors[i](states[:, i]))
                else:
                    new_actions.append(actions[:, j].detach())
    
            new_actions = torch.cat(new_actions, dim=1)
            actor_loss = -self.critics[i](states.view(batch_size, -1), new_actions).mean()
    
            # 优化Actor
            self.actor_optimizers[i].zero_grad()
            actor_loss.backward()
            self.actor_optimizers[i].step()
    
        # 软更新目标网络
        for i in range(self.n_agents):
            for param, target_param in zip(self.actors[i].parameters(), self.target_actors[i].parameters()):
                target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)
    
            for param, target_param in zip(self.critics[i].parameters(), self.target_critics[i].parameters()):
                target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/5Cy6vtc4XPdD1Lz9QsGlegJTAoRN.png)

 * 重复交互和学习过程
 * 逐渐减少探索噪声
 * 策略收敛到最优或近似最优

 * 所有智能体共享同一经验池
 * 提高数据利用效率
 * 促进策略多样性

    # 创建conda环境
    conda create -n mas python=3.8
    conda activate mas
    
    # 安装核心依赖
    pip install torch==1.9.0 numpy gym matplotlib pettingzoo
    
    # 安装多智能体环境
    pip install git+https://github.com/openai/multiagent-particle-envs.git
    
    
    bash

    import numpy as np
    import matplotlib.pyplot as plt
    from matplotlib import animation
    from multiagent.environment import MultiAgentEnv
    from multiagent.scenarios import load
    
    # 创建自定义场景
    def make_env(scenario_name, benchmark=False):
    scenario = load(f"multiagent/scenarios/{scenario_name}.py").Scenario()
    world = scenario.make_world()
    env = MultiAgentEnv(world, scenario.reset_world, scenario.reward, scenario.observation)
    return env
    
    # 协作导航场景
    class CooperativeNavigation:
    def __init__(self, num_agents=3, num_landmarks=3):
        self.num_agents = num_agents
        self.num_landmarks = num_landmarks
        self.agent_pos = np.random.uniform(-1, 1, (num_agents, 2))
        self.landmark_pos = np.random.uniform(-1, 1, (num_landmarks, 2))
        self.agent_vel = np.zeros((num_agents, 2))
        self.max_speed = 0.1
    
    def reset(self):
        self.agent_pos = np.random.uniform(-1, 1, (num_agents, 2))
        self.landmark_pos = np.random.uniform(-1, 1, (num_landmarks, 2))
        self.agent_vel = np.zeros((num_agents, 2))
        return self._get_obs()
    
    def _get_obs(self):
        obs = []
        for i in range(self.num_agents):
            agent_obs = np.concatenate([
                self.agent_pos[i],
                self.agent_vel[i],
                self.landmark_pos.ravel(),
                self.agent_pos.ravel()
            ])
            obs.append(agent_obs)
        return obs
    
    def step(self, actions):
        # 更新速度
        for i in range(self.num_agents):
            self.agent_vel[i] = np.clip(actions[i], -self.max_speed, self.max_speed)
    
        # 更新位置
        self.agent_pos += self.agent_vel
    
        # 计算奖励
        rewards = np.zeros(self.num_agents)
        for i in range(self.num_landmarks):
            dists = [np.linalg.norm(self.agent_pos[j] - self.landmark_pos[i])
                    for j in range(self.num_agents)]
            rewards -= min(dists)  # 最近距离作为奖励
    
        # 检查是否完成
        done = False
        for i in range(self.num_landmarks):
            dists = [np.linalg.norm(self.agent_pos[j] - self.landmark_pos[i])
                    for j in range(self.num_agents)]
            if min(dists) < 0.1:  # 到达阈值
                done = True
    
        return self._get_obs(), rewards, done, {}
    
    # 可视化函数
    def visualize(env, episode_length=100):
    fig = plt.figure(figsize=(8, 8))
    ax = fig.add_subplot(111)
    
    def init():
        ax.clear()
        ax.set_xlim(-1.5, 1.5)
        ax.set_ylim(-1.5, 1.5)
        return []
    
    def animate(i):
        ax.clear()
        ax.set_xlim(-1.5, 1.5)
        ax.set_ylim(-1.5, 1.5)
    
        # 绘制地标
        for landmark in env.landmark_pos:
            ax.scatter(landmark[0], landmark[1], c='red', s=100)
    
        # 绘制智能体
        for agent in env.agent_pos:
            ax.scatter(agent[0], agent[1], c='blue', s=50)
    
        return []
    
    anim = animation.FuncAnimation(fig, animate, frames=episode_length,
                                 init_func=init, blit=True, interval=100)
    plt.close()
    return anim
    
    # 训练循环
    def train_maddpg(env, num_episodes=1000):
    state_dims = [env.observation_space[i].shape[0] for i in range(env.n)]
    action_dims = [env.action_space[i].shape[0] for i in range(env.n)]
    
    maddpg = MADDPG(env.n, state_dims, action_dims)
    
    episode_rewards = []
    
    for episode in range(num_episodes):
        obs = env.reset()
        episode_reward = np.zeros(env.n)
    
        while True:
            # 选择动作
            actions = []
            for i in range(env.n):
                action = maddpg.select_action(i, obs[i])
                actions.append(action)
    
            # 执行动作
            next_obs, rewards, done, _ = env.step(actions)
    
            # 存储经验
            maddpg.buffer.push(obs, actions, rewards, next_obs, [done]*env.n)
    
            # 更新网络
            maddpg.update(batch_size=128)
    
            # 累积奖励
            episode_reward += rewards
    
            # 更新状态
            obs = next_obs
    
            if done:
                break
    
        episode_rewards.append(np.mean(episode_reward))
    
        if episode % 50 == 0:
            print(f"Episode {episode}, Avg Reward: {np.mean(episode_rewards[-50:])}")
    
    return maddpg, episode_rewards
    
    # 主程序
    if __name__ == "__main__":
    # 创建环境
    env = make_env("simple_spread")
    
    # 训练MADDPG
    maddpg, rewards = train_maddpg(env)
    
    # 可视化训练曲线
    plt.plot(rewards)
    plt.xlabel("Episode")
    plt.ylabel("Average Reward")
    plt.title("MADDPG Training Progress")
    plt.show()
    
    # 可视化最终策略
    obs = env.reset()
    frames = []
    for _ in range(100):
        actions = []
        for i in range(env.n):
            action = maddpg.select_action(i, obs[i], noise_scale=0)
            actions.append(action)
        obs, _, done, _ = env.step(actions)
        frames.append(env.render(mode="rgb_array"))
        if done:
            break
    
    # 保存动画
    anim = animation.ArtistAnimation(plt.figure(), frames, interval=50)
    anim.save("multiagent_cooperation.mp4")
    
    
    python
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-16/5vCzJNEwHTDlRcq9jMfZd6hx731O.png)

 * 训练曲线显示收敛情况
 * 动画展示智能体协作行为

 * 从数十到数百万智能体的系统
 * 分层组织架构
 * 分布式学习算法

 * 不同能力智能体的互补
 * 跨模态通信
 * 动态角色分配

 * 人类与AI智能体自然协作
 * 可解释决策
 * 信任建立机制

 * 通信复杂度随智能体数量指数增长
 * 需要新型网络架构

 * 精确评估个体贡献
 * 避免搭便车行为
 * 动态奖励 shaping

 * 鲁棒性对抗攻击
 * 安全验证
 * 故障隔离

 * 快速适应新智能体加入
 * 迁移学习能力
 * 终身学习机制

 * 结合深度学习与符号推理
 * 可解释决策
 * 逻辑约束满足

 * 量子通信协议
 * 并行决策
 * 新型博弈论框架