适用于离散动作空间的MADDPG代码

以下是适用于离散动作空间的MADDPG代码：

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

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

BUFFER_SIZE = int(1e6)  # replay buffer size
BATCH_SIZE = 128        # minibatch size
GAMMA = 0.99            # discount factor
TAU = 1e-3              # for soft update of target parameters
LR_ACTOR = 1e-3         # learning rate of the actor 
LR_CRITIC = 1e-3        # learning rate of the critic
WEIGHT_DECAY = 0        # L2 weight decay

class MADDPG:
    def __init__(self, state_size, action_size, num_agents, seed):
        self.state_size = state_size
        self.action_size = action_size
        self.num_agents = num_agents
        self.seed = random.seed(seed)

        # Actor Network (w/ Target Network)
        self.actor_local = Actor(state_size, action_size, seed).to(device)
        self.actor_target = Actor(state_size, action_size, seed).to(device)
        self.actor_optimizer = optim.Adam(self.actor_local.parameters(), lr=LR_ACTOR)

        # Critic Network (w/ Target Network)
        self.critic_local = Critic(state_size, action_size, num_agents, seed).to(device)
        self.critic_target = Critic(state_size, action_size, num_agents, seed).to(device)
        self.critic_optimizer = optim.Adam(self.critic_local.parameters(), lr=LR_CRITIC, weight_decay=WEIGHT_DECAY)

        # Noise process
        self.noise = OUNoise((num_agents, action_size), seed)

        # Replay memory
        self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)

    def act(self, states, add_noise=True):
        states = torch.from_numpy(states).float().to(device)
        actions = np.zeros((self.num_agents, self.action_size))
        self.actor_local.eval()
        with torch.no_grad():
            for agent_idx, state in enumerate(states):
                action = self.actor_local(state.unsqueeze(0)).cpu().data.numpy()
                actions[agent_idx, :] = action
        self.actor_local.train()
        if add_noise:
            actions += self.noise.sample()
        return np.clip(actions, -1, 1)

    def reset(self):
        self.noise.reset()

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

        # ---------------------------- update critic ---------------------------- #
        # Get predicted next-state actions and Q values from target models
        actions_next = torch.zeros_like(actions)
        for agent_idx, state in enumerate(next_states):
            actions_next[agent_idx, :] = self.actor_target(state.unsqueeze(0)).detach()
        actions_next = actions_next.view(-1, self.num_agents * self.action_size)
        with torch.no_grad():
            Q_targets_next = self.critic_target(next_states.view(-1, self.num_agents * self.state_size), actions_next)
        Q_targets = rewards.view(-1, self.num_agents) + (gamma * Q_targets_next * (1 - dones.view(-1, self.num_agents)))
        Q_expected = self.critic_local(states.view(-1, self.num_agents * self.state_size), actions.view(-1, self.num_agents * self.action_size))
        critic_loss = F.mse_loss(Q_expected, Q_targets)
        self.critic_optimizer.zero_grad()
        critic_loss.backward()
        torch.nn.utils.clip_grad_norm_(self.critic_local.parameters(), 1)
        self.critic_optimizer.step()

        # ---------------------------- update actor ---------------------------- #
        actions_pred = torch.zeros_like(actions)
        for agent_idx, state in enumerate(states):
            actions_pred[agent_idx, :] = self.actor_local(state.unsqueeze(0))
        actions_pred = actions_pred.view(-1, self.num_agents * self.action_size)
        actor_loss = -self.critic_local(states.view(-1, self.num_agents * self.state_size), actions_pred).mean()
        self.actor_optimizer.zero_grad()
        actor_loss.backward()
        self.actor_optimizer.step()

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

    def soft_update(self, local_model, target_model, tau):
        """Soft update model parameters."""
        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)

class Actor(nn.Module):
    def __init__(self, state_size, action_size, seed):
        super(Actor, 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)
        self.bn1 = nn.BatchNorm1d(64)
        self.bn2 = nn.BatchNorm1d(64)
        
    def forward(self, state):
        x = F.relu(self.bn1(self.fc1(state)))
        x = F.relu(self.bn2(self.fc2(x)))
        return torch.tanh(self.fc3(x))

class Critic(nn.Module):
    def __init__(self, state_size, action_size, num_agents, seed):
        super(Critic, self).__init__()
        self.seed = torch.manual_seed(seed)
        self.fcs1 = nn.Linear(num_agents*state_size, 64)
        self.fc2 = nn.Linear(64+num_agents*action_size, 64)
        self.fc3 = nn.Linear(64, 1)
        self.bn1 = nn.BatchNorm1d(64)
        self.bn2 = nn.BatchNorm1d(64)
        
    def forward(self, state, action):
        xs = F.relu(self.bn1(self.fcs1(state)))
        x = torch.cat((xs, action), dim=1)
        x = F.relu(self.bn2(self.fc2(x)))
        return self.fc3(x)

class OUNoise:
    """Ornstein-Uhlenbeck process."""

    def __init__(self, size, seed, mu=0., theta=0.15, sigma=0.2):
        """Initialize parameters and noise process."""
        self.mu = mu * np.ones(size)
        self.theta = theta
        self.sigma = sigma
        self.seed = random.seed(seed)
        self.reset()

    def reset(self):
        """Reset the internal state (= noise) to mean (mu)."""
        self.state = np.copy(self.mu)

    def sample(self):
        """Update internal state and return it as a noise sample."""
        x = self.state
        dx = self.theta * (self.mu - x) + self.sigma * np.random.standard_normal(self.mu.shape)
        self.state = x + dx
        return self.state

class ReplayBuffer:
    """Fixed-size buffer to store experience tuples."""

    def __init__(self, action_size, buffer_size, batch_size, seed):
        """Initialize a ReplayBuffer object.

        Params
        ======
            action_size (int): dimension of each action
            buffer_size (int): maximum size of buffer
            batch_size (int): size of each training batch
            seed (int): random seed
        """
        self.action_size = action_size
        self.memory = deque(maxlen=buffer_size)  # internal memory (deque)
        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):
        """Add a new experience to memory."""
        e = self.experience(state, action, reward, next_state, done)
        self.memory.append(e)

    def sample(self):
        """Randomly sample a batch of experiences from memory."""
        experiences = random.sample(self.memory, k=self.batch_size)

        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])).float().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):
        """Return the current size of internal memory."""
        return len(self.memory)

在上述代码中，我们定义了一个名为“MADDPG”的类，该类包含了用于多智能体强化学习的所有组件，包括Actor和Critic神经网络、经验回放缓冲区、Ornstein-Uhlenbeck噪声过程等。

我们还定义了Actor和Critic神经网络模型，这些模型的结构与单智能体强化学习中的模型类似，但是我们需要为每个智能体定义一个单独的Actor和Critic。此外，我们还定义了一个Ornstein-Uhlenbeck噪声过程，用于添加随机性。

在主循环中，我们使用ReplayBuffer来存储智能体的经验，并定期从中随机抽取一个批次进行训练。在训练中，我们使用MADDPG算法更新Critic和Actor模型，从而使智能体能够逐渐优化其策略。