Pytorch程序实现

这里是一个简单的PyTorch程序实现MAPPO算法的示例：

首先，我们需要定义一个模型来表示每个智能体的策略。这里我们使用一个简单的神经网络模型来表示策略：

import torch
import torch.nn as nn
import torch.nn.functional as F

class PolicyModel(nn.Module):
    def __init__(self, obs_dim, act_dim):
        super(PolicyModel, self).__init__()
        self.fc1 = nn.Linear(obs_dim, 64)
        self.fc2 = nn.Linear(64, 64)
        self.fc3 = nn.Linear(64, act_dim)

    def forward(self, obs):
        x = F.relu(self.fc1(obs))
        x = F.relu(self.fc2(x))
        x = F.softmax(self.fc3(x), dim=-1)
        return x

接下来，我们需要定义MAPPO算法的核心部分——优化器。这里我们使用PyTorch中的Adam优化器，并定义一个`compute_loss`函数来计算每个智能体的损失值：

import torch.optim as optim

class MAPPO:
    def __init__(self, obs_dim, act_dim, num_agents):
        self.num_agents = num_agents
        self.policy_model = PolicyModel(obs_dim, act_dim)
        self.optimizer = optim.Adam(self.policy_model.parameters(), lr=3e-4)

    def compute_loss(self, obs, act, adv, old_log_prob):
        log_prob = torch.log(self.policy_model(obs))
        prob_ratio = torch.exp(log_prob - old_log_prob)
        surr1 = prob_ratio * adv
        surr2 = torch.clamp(prob_ratio, 1 - 0.2, 1 + 0.2) * adv
        policy_loss = -torch.min(surr1, surr2).mean()

        entropy = -(log_prob * torch.exp(log_prob)).sum(-1).mean()

        return policy_loss - 0.01 * entropy

    def update(self, observations, actions, advantages, old_log_probs):
        observations = torch.Tensor(observations)
        actions = torch.Tensor(actions)
        advantages = torch.Tensor(advantages)
        old_log_probs = torch.Tensor(old_log_probs)

        batch_size = observations.shape[0]
        batch_idx = np.arange(batch_size)

        for _ in range(10):
            np.random.shuffle(batch_idx)

            for start in range(0, batch_size, self.num_agents):
                end = start + self.num_agents
                mb_idx = batch_idx[start:end]

                obs_batch = observations[mb_idx]
                act_batch = actions[mb_idx]
                adv_batch = advantages[mb_idx]
                old_log_prob_batch = old_log_probs[mb_idx]

                loss = self.compute_loss(obs_batch, act_batch, adv_batch, old_log_prob_batch)

                self.optimizer.zero_grad()
                loss.backward()
                self.optimizer.step()

最后，我们需要定义一个主函数来使用MAPPO算法训练智能体：

def main():
    obs_dim = 10
    act_dim = 2
    num_agents = 4

    mappo = MAPPO(obs_dim, act_dim, num_agents)

    for _ in range(1000):
        # 在环境中采样数据
        observations, actions, rewards, next_observations, dones = sample_data()

        # 计算每个智能体的优势值和旧的对数概率值
        advantages, old_log_probs = compute_advantages_and_old_log_probs(observations, actions, rewards, next_observations, dones, mappo.policy_model)

        # 更新策略网络
        mappo.update(observations, actions, advantages, old_log_probs)

当然，这只是一个简单的示例，实际中还需要根据具体的问题和应用场景进行一定的修改和调整。