进行maddpg算法时如何将action的范围设置为0到1

在使用MADDPG算法时，可以通过对每个智能体的动作输出进行限制，将其范围缩放到[0, 1]之间。具体方法如下：

1. 对于每个智能体的动作输出，先将其映射到[-1, 1]之间，可以使用tanh函数实现。

2. 接着，将映射到[-1, 1]之间的动作输出线性缩放到[0, 1]之间。具体地，将[-1, 1]之间的数值线性映射到[0, 1]之间的数值，即将原数值加1后除以2。

通过这种方式，可以将每个智能体的动作输出限制在[0, 1]之间，方便MADDPG算法的训练和优化。

相关问题

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

maddpg算法pytorch实例讲解

MADDPG是一种多智能体强化学习算法，它是DDPG的扩展版本。DDPG是一种深度强化学习算法，用于解决连续动作空间的问题。在MADDPG中，每个智能体都具有自己的actor和critic网络。这些网络被用来学习不同的策略，并且可以共享经验池中的经验。在此过程中，每个智能体都能够观察到其他智能体的状态，并且可以考虑其他智能体的行为。

下面是一个使用Pytorch实现MADDPG算法的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from collections import deque

# 神经网络定义
class Actor(nn.Module):
    def __init__(self, state_dim, action_dim, max_action):
        super(Actor, self).__init__()

        self.layer_1 = nn.Linear(state_dim, 256)
        self.layer_2 = nn.Linear(256, 256)
        self.layer_3 = nn.Linear(256, action_dim)
        self.max_action = max_action

    def forward(self, x):
        x = torch.relu(self.layer_1(x))
        x = torch.relu(self.layer_2(x))
        x = self.max_action * torch.tanh(self.layer_3(x))
        return x

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

        self.layer_1 = nn.Linear(state_dim + action_dim, 256)
        self.layer_2 = nn.Linear(256, 256)
        self.layer_3 = nn.Linear(256, 1)

    def forward(self, x, u):
        xu = torch.cat([x, u], 1)
        xu = torch.relu(self.layer_1(xu))
        xu = torch.relu(self.layer_2(xu))
        xu = self.layer_3(xu)
        return xu

# 经验回放缓冲区
class ReplayBuffer:
    def __init__(self, max_size):
        self.buffer = deque(maxlen=max_size)

    def add(self, state, action, reward, next_state, done):
        self.buffer.append((state, action, reward, next_state, done))

    def sample(self, batch_size):
        state, action, reward, next_state, done = zip(*np.random.choice(self.buffer, batch_size, replace=False))
        return np.concatenate(state), np.concatenate(action), np.array(reward, dtype=np.float32), np.concatenate(next_state), np.array(done, dtype=np.uint8)

    def __len__(self):
        return len(self.buffer)

# MADDPG算法
class MADDPG:
    def __init__(self, state_dim, action_dim, max_action, discount=0.99, tau=0.01):
        self.discount = discount
        self.tau = tau
        self.memory = ReplayBuffer(1000000)
        self.actor = Actor(state_dim, action_dim, max_action)
        self.actor_target = Actor(state_dim, action_dim, max_action)
        self.critic = Critic(state_dim, action_dim)
        self.critic_target = Critic(state_dim, action_dim)
        self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=0.001)
        self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=0.001)

    def get_action(self, state):
        state = torch.FloatTensor(state.reshape(1, -1))
        return self.actor(state).detach().numpy()[0]

    def update(self, batch_size):
        state, action, reward, next_state, done = self.memory.sample(batch_size)
        state = torch.FloatTensor(state)
        action = torch.FloatTensor(action)
        reward = torch.FloatTensor(reward).unsqueeze(1)
        next_state = torch.FloatTensor(next_state)
        done = torch.FloatTensor(done).unsqueeze(1)

        # 更新critic网络
        next_actions = []
        for i in range(action.shape[0]):
            next_actions.append(self.actor_target(torch.FloatTensor(next_state[i]).unsqueeze(0)))
        next_actions = torch.cat(next_actions, dim=0)
        target_Q = self.critic_target(next_state, next_actions).detach()
        y = reward + (1 - done) * self.discount * target_Q
        current_Q = self.critic(state, action)
        critic_loss = nn.MSELoss()(current_Q, y)
        self.critic_optimizer.zero_grad()
        critic_loss.backward()
        self.critic_optimizer.step()

        # 更新actor网络
        actions = []
        for i in range(action.shape[0]):
            actions.append(self.actor(torch.FloatTensor(state[i]).unsqueeze(0)))
        actions = torch.cat(actions, dim=0)
        actor_loss = -self.critic(state, actions).mean()
        self.actor_optimizer.zero_grad()
        actor_loss.backward()
        self.actor_optimizer.step()

        # 更新target网络
        for target_param, param in zip(self.actor_target.parameters(), self.actor.parameters()):
            target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)

        for target_param, param in zip(self.critic_target.parameters(), self.critic.parameters()):
            target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)

# 训练
def train(env, agent, episodes, batch_size):
    for episode in range(episodes):
        state = env.reset()
        done = False
        while not done:
            action = agent.get_action(state)
            next_state, reward, done, _ = env.step(action)
            agent.memory.add(state, action, reward, next_state, done)
            state = next_state

            if len(agent.memory) > batch_size:
                agent.update(batch_size)

env = gym.make('MultiAgentPendulum-v0')
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.shape[0]
max_action = float(env.action_space.high[0])
agent = MADDPG(state_dim, action_dim, max_action)
train(env, agent, 10000, 256)

这段代码中，我们首先定义了Actor和Critic神经网络，然后定义了经验回放缓冲区。接着，我们实现了MADDPG算法，并在训练函数中使用该算法。在每个episode中，我们首先将环境重置，并在每个时间步中获取智能体的动作。然后，我们将经验添加到回放缓冲区中，并在缓冲区中积累一定的经验后，使用MADDPG算法更新每个智能体的actor和critic网络。