多智能体DQN代码Pytorch

以下是多智能体DQN的Pytorch代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import numpy as np
import random
from collections import namedtuple, deque

# 定义超参数
BUFFER_SIZE = int(1e5)  # replay buffer的大小
BATCH_SIZE = 64         # mini-batch的大小
GAMMA = 0.99            # discount factor
TAU = 1e-3              # soft update的参数
LR = 5e-4               # learning rate
UPDATE_EVERY = 4        # 更新的时间间隔

# 检查GPU是否可用
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")


# 定义多智能体DQN的类
class MADQNAgent:
    
    def __init__(self, state_size, action_size, num_agents, seed):
        """初始化多智能体DQN的类
        
        参数：
            state_size：每个智能体状态的维度
            action_size：每个智能体动作的维度
            num_agents：智能体的数量
            seed：随机数生成器的种子
        """
        self.state_size = state_size
        self.action_size = action_size
        self.num_agents = num_agents
        self.seed = random.seed(seed)
        
        # 定义Q网络和target网络
        self.qnetwork_local = QNetwork(state_size, action_size, seed).to(device)
        self.qnetwork_target = QNetwork(state_size, action_size, seed).to(device)
        self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
        
        # 定义replay buffer
        self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
        
        # 定义时间步数
        self.t_step = 0
    
    def step(self, state, action, reward, next_state, done):
        # 添加经验到replay buffer中
        self.memory.add(state, action, reward, next_state, done)
        
        # 更新时间步数
        self.t_step = (self.t_step + 1) % UPDATE_EVERY
        
        # 如果replay buffer中的样本数大于BATCH_SIZE，并且到了更新时间间隔，则进行学习
        if len(self.memory) > BATCH_SIZE and self.t_step == 0:
            experiences = self.memory.sample()
            self.learn(experiences, GAMMA)
    
    def act(self, state, eps=0.):
        # 选择动作
        state = torch.from_numpy(state).float().to(device)
        self.qnetwork_local.eval()
        with torch.no_grad():
            action_values = self.qnetwork_local(state)
        self.qnetwork_local.train()
        
        # 添加噪声
        action_values = action_values.cpu().data.numpy()
        actions = np.zeros((self.num_agents, self.action_size))
        for i in range(self.num_agents):
            actions[i] = np.argmax(action_values[i])
            if random.random() < eps:
                actions[i] = random.choice(np.arange(self.action_size))
        return actions
    
    def learn(self, experiences, gamma):
        states, actions, rewards, next_states, dones = experiences
        
        # 计算Q值
        Q_targets_next = self.qnetwork_target(next_states).detach().max(1)[0].unsqueeze(1)
        Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
        Q_expected = self.qnetwork_local(states).gather(1, actions)
        
        # 计算误差，并更新Q网络
        loss = F.mse_loss(Q_expected, Q_targets)
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()
        
        # 更新target网络
        self.soft_update(self.qnetwork_local, self.qnetwork_target, 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 + (1.0-tau)*target_param.data)
            

# 定义Q网络的类
class QNetwork(nn.Module):
    
    def __init__(self, state_size, action_size, seed):
        """初始化Q网络的类
        
        参数：
            state_size：状态的维度
            action_size：动作的维度
            seed：随机数生成器的种子
        """
        super(QNetwork, self).__init__()
        self.seed = torch.manual_seed(seed)
        self.fc1 = nn.Linear(state_size, 64)
        self.fc2 = nn.Linear(64, 64)
        self.fc3 = nn.Linear(64, action_size)
        
    def forward(self, state):
        """前向传播
        
        参数：
            state：状态
        
        返回：
            给定状态下的Q值
        """
        x = F.relu(self.fc1(state))
        x = F.relu(self.fc2(x))
        return self.fc3(x)
    

# 定义replay buffer的类
class ReplayBuffer:
    
    def __init__(self, action_size, buffer_size, batch_size, seed):
        """初始化replay buffer的类
        
        参数：
            action_size：动作的维度
            buffer_size：replay buffer的大小
            batch_size：mini-batch的大小
            seed：随机数生成器的种子
        """
        self.action_size = action_size
        self.memory = deque(maxlen=buffer_size)
        self.batch_size = batch_size
        self.experience = namedtuple("Experience", field_names=["state", "action", "reward", "next_state", "done"])
        self.seed = random.seed(seed)
    
    def add(self, state, action, reward, next_state, done):
        """添加经验到replay buffer中"""
        e = self.experience(state, action, reward, next_state, done)
        self.memory.append(e)
    
    def sample(self):
        """从replay buffer中随机采样"""
        experiences = random.sample(self.memory, k=self.batch_size)
        
        # 将经验转换为tensor
        states = torch.from_numpy(np.vstack([e.state for e in experiences if e is not None])).float().to(device)
        actions = torch.from_numpy(np.vstack([e.action for e in experiences if e is not None])).long().to(device)
        rewards = torch.from_numpy(np.vstack([e.reward for e in experiences if e is not None])).float().to(device)
        next_states = torch.from_numpy(np.vstack([e.next_state for e in experiences if e is not None])).float().to(device)
        dones = torch.from_numpy(np.vstack([e.done for e in experiences if e is not None]).astype(np.uint8)).float().to(device)
        
        return (states, actions, rewards, next_states, dones)
    
    def __len__(self):
        """返回replay buffer中的样本数"""
        return len(self.memory)

注意：这是多智能体DQN的代码，与普通DQN的代码略有不同。需要构建多个智能体的Q网络，并且在学习时需要对每个智能体的经验进行采样和更新。