maddpg算法pytorch实现两个智能体

好的，我可以为您提供一些指导。首先，您需要确定MADDPG算法的网络结构和超参数。然后，您可以使用PyTorch框架来实现网络。以下是一个简单的示例代码，可以帮助您开始：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

class Actor(nn.Module):
    def __init__(self, state_dim, action_dim, hidden_dim):
        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)

    def forward(self, state):
        x = torch.relu(self.fc1(state))
        x = torch.relu(self.fc2(x))
        x = torch.tanh(self.fc3(x))
        return x

class Critic(nn.Module):
    def __init__(self, state_dim, action_dim, hidden_dim):
        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)

    def forward(self, state, action):
        x = torch.cat([state, action], dim=1)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

class MADDPG:
    def __init__(self, state_dim, action_dim, hidden_dim, lr, gamma, tau):
        self.actor_local = Actor(state_dim, action_dim, hidden_dim)
        self.actor_target = Actor(state_dim, action_dim, hidden_dim)
        self.critic_local = Critic(state_dim, action_dim, hidden_dim)
        self.critic_target = Critic(state_dim, action_dim, hidden_dim)
        self.actor_optimizer = optim.Adam(self.actor_local.parameters(), lr=lr)
        self.critic_optimizer = optim.Adam(self.critic_local.parameters(), lr=lr)
        self.gamma = gamma
        self.tau = tau

    def act(self, state):
        state = torch.FloatTensor(state)
        action = self.actor_local(state).detach().numpy()
        return np.clip(action, -1, 1)

    def update(self, experiences):
        states, actions, rewards, next_states, dones = experiences

        # update critic
        next_actions = [self.actor_target(next_state) for next_state in next_states]
        next_actions = torch.cat(next_actions, dim=1)
        Q_targets_next = self.critic_target(next_states, next_actions)
        Q_targets = rewards + (self.gamma * Q_targets_next * (1 - dones))
        Q_expected = self.critic_local(states, actions)
        critic_loss = nn.MSELoss()(Q_expected, Q_targets.detach())
        self.critic_optimizer.zero_grad()
        critic_loss.backward()
        self.critic_optimizer.step()

        # update actor
        actions_pred = [self.actor_local(state) for state in states]
        actions_pred = torch.cat(actions_pred, dim=1)
        actor_loss = -self.critic_local(states, actions_pred).mean()
        self.actor_optimizer.zero_grad()
        actor_loss.backward()
        self.actor_optimizer.step()

        # update target networks
        self.soft_update(self.actor_local, self.actor_target, self.tau)
        self.soft_update(self.critic_local, self.critic_target, self.tau)

    def soft_update(self, local_model, target_model, tau):
        for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
            target_param.data.copy_(tau * local_param.data + (1 - tau) * target_param.data)

在这个示例代码中，我们定义了Actor和Critic网络，并实现了MADDPG算法的主要功能。您可以根据需要调整网络结构和超参数。