maddpg代码实现

MADDPG（Multi-Agent Deep Deterministic Policy Gradient）是一种用于解决多智能体强化学习问题的算法。它是对DDPG（Deep Deterministic Policy Gradient）算法的扩展，主要用于处理多个智能体在同一环境中协作或竞争的情况。

MADDPG算法的实现需要以下步骤：

1. 环境设置：首先，需要确定每个智能体的观察空间和动作空间。每个智能体可以观察到环境的局部信息，并采取相应的动作。

2. 神经网络模型：为每个智能体设计一个神经网络模型，用于学习策略。通常，模型由两个部分组成：一个用于策略学习的Actor模型和一个用于Q值估计的Critic模型。

3. 经验重放缓冲区：构建一个共享的经验重放缓冲区，用于存储智能体的经验。每个智能体根据当前策略采样并执行动作，然后将经验存储在缓冲区中。

4. 训练过程：在每个时间步骤中，从经验重放缓冲区中随机采样一批经验样本。然后，利用这些经验样本来计算Actor和Critic的损失函数，并通过反向传播更新网络参数。

5. 算法改进技巧：为了提高算法的性能，可以采用一些改进技巧。例如，引入目标网络来稳定训练过程，使用分布式优化方法来加速算法收敛等等。

通过上述步骤，可以实现MADDPG算法，使多个智能体能够在协作或竞争的环境中进行学习和决策。这种算法可以应用于多个领域，如协同控制、多智能体游戏等。

相关问题

per-maddpg代码

以下是基于PyTorch实现的Per-MADDPG算法的代码示例：

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

# Define the Actor network
class Actor(nn.Module):
    def __init__(self, state_dim, action_dim, hidden_dims=[64, 64]):
        super(Actor, self).__init__()

        self.layers = nn.ModuleList()
        self.layers.append(nn.Linear(state_dim, hidden_dims[0]))
        for i in range(1, len(hidden_dims)):
            self.layers.append(nn.Linear(hidden_dims[i-1], hidden_dims[i]))
        self.layers.append(nn.Linear(hidden_dims[-1], action_dim))

    def forward(self, state):
        x = state
        for layer in self.layers[:-1]:
            x = F.relu(layer(x))
        x = torch.tanh(self.layers[-1](x))
        return x

# Define the Critic network
class Critic(nn.Module):
    def __init__(self, state_dim, action_dim, hidden_dims=[64, 64]):
        super(Critic, self).__init__()

        self.layers = nn.ModuleList()
        self.layers.append(nn.Linear(state_dim + action_dim, hidden_dims[0]))
        for i in range(1, len(hidden_dims)):
            self.layers.append(nn.Linear(hidden_dims[i-1], hidden_dims[i]))
        self.layers.append(nn.Linear(hidden_dims[-1], 1))

    def forward(self, state, action):
        x = torch.cat([state, action], dim=1)
        for layer in self.layers[:-1]:
            x = F.relu(layer(x))
        x = self.layers[-1](x)
        return x

# Define the Replay Buffer
class ReplayBuffer:
    def __init__(self, max_size):
        self.max_size = max_size
        self.buffer = []
        self.idx = 0

    def add(self, state, action, reward, next_state, done):
        experience = (state, action, reward, next_state, done)
        if len(self.buffer) < self.max_size:
            self.buffer.append(experience)
        else:
            self.buffer[self.idx] = experience
            self.idx = (self.idx + 1) % self.max_size

    def sample(self, batch_size):
        samples = np.random.choice(len(self.buffer), batch_size, replace=False)
        states, actions, rewards, next_states, dones = zip(*[self.buffer[idx] for idx in samples])
        return np.stack(states), np.stack(actions), \
               np.stack(rewards), np.stack(next_states), \
               np.stack(dones)

# Define the Per-MADDPG agent
class PerMADDPG:
    def __init__(self, state_dim, action_dim, num_agents, gamma=0.99, tau=0.01,
                 lr_actor=0.001, lr_critic=0.001, buffer_size=int(1e6),
                 batch_size=64, alpha=0.6, beta=0.4, eps=1e-5):
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.num_agents = num_agents
        self.gamma = gamma
        self.tau = tau
        self.lr_actor = lr_actor
        self.lr_critic = lr_critic
        self.batch_size = batch_size
        self.alpha = alpha
        self.beta = beta
        self.eps = eps

        self.actors = [Actor(state_dim, action_dim) for _ in range(num_agents)]
        self.critics = [Critic(state_dim*num_agents, action_dim*num_agents) for _ in range(num_agents)]

        self.target_actors = [Actor(state_dim, action_dim) for _ in range(num_agents)]
        self.target_critics = [Critic(state_dim*num_agents, action_dim*num_agents) for _ in range(num_agents)]

        for i in range(num_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())

        self.actor_optimizers = [optim.Adam(actor.parameters(), lr=lr_actor) for actor in self.actors]
        self.critic_optimizers = [optim.Adam(critic.parameters(), lr=lr_critic) for critic in self.critics]

        self.replay_buffer = ReplayBuffer(max_size=buffer_size)

    def act(self, states, noise=0.0):
        actions = []
        for i in range(self.num_agents):
            state = torch.tensor(states[i], dtype=torch.float32)
            action = self.actors[i](state.unsqueeze(0)).squeeze(0).detach().numpy()
            action += noise * np.random.randn(self.action_dim)
            actions.append(np.clip(action, -1.0, 1.0))
        return actions

    def update(self):
        # Sample a batch of experiences from the replay buffer
        states, actions, rewards, next_states, dones = self.replay_buffer.sample(self.batch_size)

        # Convert to PyTorch tensors
        states = torch.tensor(states, dtype=torch.float32)
        actions = torch.tensor(actions, dtype=torch.float32)
        rewards = torch.tensor(rewards, dtype=torch.float32).unsqueeze(1)
        next_states = torch.tensor(next_states, dtype=torch.float32)
        dones = torch.tensor(dones, dtype=torch.float32).unsqueeze(1)

        # Compute the TD error
        target_actions = []
        for i in range(self.num_agents):
            target_actions.append(self.target_actors[i](next_states[:, i, :]))
        target_actions = torch.stack(target_actions, dim=1)
        target_q_values = []
        for i in range(self.num_agents):
            target_q_values.append(self.target_critics[i](next_states.view(-1, self.state_dim*self.num_agents), target_actions.view(-1, self.action_dim*self.num_agents)))
        target_q_values = torch.stack(target_q_values, dim=1)
        target_q_values = rewards[:, :, None] + self.gamma * (1 - dones[:, :, None]) * target_q_values
        predicted_q_values = []
        for i in range(self.num_agents):
            predicted_q_values.append(self.critics[i](states.view(-1, self.state_dim*self.num_agents), actions.view(-1, self.action_dim*self.num_agents)))
        predicted_q_values = torch.stack(predicted_q_values, dim=1)
        td_errors = target_q_values - predicted_q_values

        # Update the priorities in the replay buffer
        priorities = np.abs(td_errors.detach().numpy()) ** self.alpha + self.eps
        for i in range(self.batch_size):
            idx = self.replay_buffer.idx - self.batch_size + i
            self.replay_buffer.buffer[idx] = (states[i], actions[i], rewards[i], next_states[i], dones[i], priorities[i])

        # Compute the importance-sampling weights
        weights = (self.replay_buffer.max_size * priorities) ** (-self.beta)
        weights /= np.max(weights)

        # Update the actor and critic networks
        for i in range(self.num_agents):
            # Sample a minibatch of experiences from the replay buffer
            idxs = np.random.randint(0, len(self.replay_buffer.buffer), size=self.batch_size)
            states_mb = []
            actions_mb = []
            weights_mb = []
            td_errors_mb = []
            for j in range(self.batch_size):
                state, action, reward, next_state, done, priority = self.replay_buffer.buffer[idxs[j]]
                states_mb.append(state)
                actions_mb.append(action)
                weights_mb.append(weights[idxs[j]])
                td_errors_mb.append(td_errors[j, i].item())

            # Convert to PyTorch tensors
            states_mb = torch.tensor(states_mb, dtype=torch.float32)
            actions_mb = torch.tensor(actions_mb, dtype=torch.float32)
            weights_mb = torch.tensor(weights_mb, dtype=torch.float32).unsqueeze(1)
            td_errors_mb = torch.tensor(td_errors_mb, dtype=torch.float32).unsqueeze(1)

            # Update the critic network
            self.critic_optimizers[i].zero_grad()
            predicted_q_values_mb = self.critics[i](states_mb.view(-1, self.state_dim*self.num_agents), actions_mb.view(-1, self.action_dim*self.num_agents))
            critic_loss = torch.mean(weights_mb * (predicted_q_values_mb - target_q_values[:, i, None]).pow(2))
            critic_loss.backward()
            self.critic_optimizers[i].step()

            # Update the actor network
            self.actor_optimizers[i].zero_grad()
            actor_loss = -torch.mean(weights_mb * td_errors_mb.detach() * self.actors[i](states_mb))
            actor_loss.backward()
            self.actor_optimizers[i].step()

            # Update the target networks
            for target_param, param in zip(self.target_actors[i].parameters(), self.actors[i].parameters()):
                target_param.data.copy_(self.tau * param + (1 - self.tau) * target_param)
            for target_param, param in zip(self.target_critics[i].parameters(), self.critics[i].parameters()):
                target_param.data.copy_(self.tau * param + (1 - self.tau) * target_param)

    def save(self, filename):
        torch.save({
            'actor_params': [actor.state_dict() for actor in self.actors],
            'critic_params': [critic.state_dict() for critic in self.critics]
        }, filename)

    def load(self, filename):
        checkpoint = torch.load(filename)
        for i in range(self.num_agents):
            self.actors[i].load_state_dict(checkpoint['actor_params'][i])
            self.critics[i].load_state_dict(checkpoint['critic_params'][i])
            self.target_actors[i].load_state_dict(checkpoint['actor_params'][i])
            self.target_critics[i].load_state_dict(checkpoint['critic_params'][i])

在上述代码中，`Actor` 类定义了 Actor 网络，`Critic` 类定义了 Critic 网络，`ReplayBuffer` 类定义了经验回放缓存，`PerMADDPG` 类实现了 Per-MADDPG 算法。

在 `PerMADDPG` 类的 `__init__` 函数中，我们定义了模型的超参数，创建了 Actor 和 Critic 网络，以及目标网络和优化器，并初始化了经验回放缓存。

在 `act` 函数中，我们使用 Actor 网络生成动作，加入一定的高斯噪声。

在 `update` 函数中，首先从经验回放缓存中采样一批经验，计算 TD 误差，并更新缓存中的优先级。然后，计算重要性采样权重，并使用这些权重更新 Actor 和 Critic 网络。最后，更新目标网络。

最后，`save` 函数和 `load` 函数分别用于保存和加载模型的参数。

MADDPG算法实现

MADDPG算法的实现步骤如下：

1.初始化神经网络参数和经验回放缓冲区。

2.对于每个智能体，执行以下操作：

（1）根据当前状态，使用Actor网络选择动作。

（2）执行动作并观察环境反馈。

（3）将经验元组（当前状态，动作，奖励，下一个状态）存储在经验回放缓冲区中。

（4）从经验回放缓冲区中随机采样一批经验元组。

（5）使用Critic网络计算TD误差。

（6）使用TD误差训练Critic网络。

（7）使用Actor网络计算动作梯度。

（8）使用动作梯度训练Actor网络。

3.重复执行步骤2，直到达到预设的训练次数或者智能体已经学会了任务。

# 以下是MADDPG算法的Python实现代码

# 初始化神经网络参数和经验回放缓冲区
agent1 = Agent(state_size, action_size, random_seed=0)
agent2 = Agent(state_size, action_size, random_seed=0)
memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, random_seed=0)

# 训练智能体
for i_episode in range(1, n_episodes+1):
    env_info = env.reset(train_mode=True)[brain_name]
    state = np.concatenate((env_info.vector_observations[0], env_info.vector_observations[1]))
    score = np.zeros(num_agents)
    for t in range(max_t):
        action1 = agent1.act(state, add_noise=True)
        action2 = agent2.act(state, add_noise=True)
        action = np.concatenate((action1, action2))
        env_info = env.step(action)[brain_name]
        next_state = np.concatenate((env_info.vector_observations[0], env_info.vector_observations[1]))
        reward = env_info.rewards
        done = env_info.local_done
        memory.add(state, action, reward, next_state, done)
        if len(memory) > BATCH_SIZE:
            experiences = memory.sample()
            agent1.learn(experiences, GAMMA)
            agent2.learn(experiences, GAMMA)
        state = next_state
        score += reward
        if np.any(done):
            break
    scores_deque.append(np.max(score))
    scores.append(np.max(score))
    print('\rEpisode {}\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_deque)), end="")
    if i_episode % 100 == 0:
        print('\rEpisode {}\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_deque)))
    if np.mean(scores_deque)>=0.5:
        print('\nEnvironment solved in {:d} episodes!\tAverage Score: {:.2f}'.format(i_episode-100, np.mean(scores_deque)))
        torch.save(agent1.actor_local.state_dict(), 'checkpoint_actor1.pth')
        torch.save(agent1.critic_local.state_dict(), 'checkpoint_critic1.pth')
        torch.save(agent2.actor_local.state_dict(), 'checkpoint_actor2.pth')
        torch.save(agent2.critic_local.state_dict(), 'checkpoint_critic2.pth')
        break