MPE+MADDPG

MPE是OpenAI开源的多智能体环境，而MADDPG是一种用于多智能体强化学习的算法。MADDPG算法是基于DDPG算法的改进，它使用了Actor-Critic框架，每个智能体都有一个Actor和一个Critic网络。在MADDPG中，每个智能体的Actor网络输出一个动作，而Critic网络则根据所有智能体的状态和动作输出一个Q值。MADDPG算法通过协同训练多个智能体，使得每个智能体的Actor网络能够学习到对整个系统最优的策略。同时，MADDPG算法还引入了Advantage Actor-Critic算法，用于更好地处理多智能体环境中的奖励分配问题。

在MPE环境中，可以使用MADDPG算法来训练多个智能体，使得它们能够协同完成任务。通过MADDPG算法，每个智能体可以根据自己的状态和其他智能体的状态来选择最优的动作，从而实现协同行为。同时，MADDPG算法还可以处理多智能体环境中的奖励分配问题，使得每个智能体都能够获得合理的奖励。

相关问题

MADDPG pytorch

MADDPG pytorch是一个基于PyTorch框架实现的多智能体强化学习算法。它是一种深度强化学习算法，可以用于解决多智能体协作问题。MADDPG pytorch的实现可以在GitHub上找到，同时也提供了环境配置过程和使用方法。在使用MADDPG pytorch之前，需要先激活Python3.6环境，并将MPE环境下载并解压到虚拟环境中。然后，将MPE环境中的multiagent文件夹拷贝到MADDPG工程目录下，并在PyCharm中将common文件夹Mark Directory as Source Root，以便MADDPG工程能够较好地调用multiagent中创建的环境。最后，运行main即可开始使用MADDPG pytorch进行多智能体协作问题的解决。

maddpg结合优先经验回放和mpe环境 tensorflow代码

以下是使用TensorFlow实现MADDPG算法并结合优先经验回放和MPE环境的代码。代码中包含注释以帮助理解。

import numpy as np
import tensorflow as tf
import random

from mlagents.envs import UnityEnvironment
from mlagents.envs import BrainInfo

# 超参数
BUFFER_SIZE = int(1e6)  # 经验回放缓冲区大小
BATCH_SIZE = 128  # 批量大小
GAMMA = 0.99  # 折扣因子
TAU = 1e-3  # 目标网络更新速率
LR_ACTOR = 1e-3  # Actor网络学习率
LR_CRITIC = 1e-3  # Critic网络学习率
UPDATE_EVERY = 2  # 更新网络的时间步数
NUM_UPDATES = 10  # 每次更新网络的次数

# 神经网络模型
class Actor(tf.keras.Model):
    def __init__(self, state_size, action_size):
        super(Actor, self).__init__()
        self.fc1 = tf.keras.layers.Dense(256, activation='relu')
        self.fc2 = tf.keras.layers.Dense(128, activation='relu')
        self.fc3 = tf.keras.layers.Dense(action_size, activation='tanh')
        
    def call(self, state):
        x = self.fc1(state)
        x = self.fc2(x)
        x = self.fc3(x)
        return x
    
class Critic(tf.keras.Model):
    def __init__(self, state_size, action_size):
        super(Critic, self).__init__()
        self.fc1 = tf.keras.layers.Dense(256, activation='relu')
        self.fc2 = tf.keras.layers.Dense(128, activation='relu')
        self.fc3 = tf.keras.layers.Dense(1, activation=None)
        
        self.fc4 = tf.keras.layers.Dense(256, activation='relu')
        self.fc5 = tf.keras.layers.Dense(128, activation='relu')
        self.fc6 = tf.keras.layers.Dense(1, activation=None)
        
    def call(self, state, action):
        xs = tf.concat([state, action], axis=1)
        x1 = self.fc1(xs)
        x1 = self.fc2(x1)
        x1 = self.fc3(x1)
        
        x2 = self.fc4(xs)
        x2 = self.fc5(x2)
        x2 = self.fc6(x2)
        
        return x1, x2
    
# 优先经验回放类
class PrioritizedReplay:
    def __init__(self, buffer_size, batch_size):
        self.buffer_size = buffer_size
        self.batch_size = batch_size
        self.buffer = []
        self.priorities = np.zeros((buffer_size,), dtype=np.float32)
        self.pos = 0
        self.alpha = 0.5
        self.beta = 0.5
        self.beta_increment_per_sampling = 0.001
        
    def add(self, state, action, reward, next_state, done):
        max_priority = np.max(self.priorities) if self.buffer else 1.0
        experience = (state, action, reward, next_state, done)
        if len(self.buffer) < self.buffer_size:
            self.buffer.append(experience)
        else:
            self.buffer[self.pos] = experience
        self.priorities[self.pos] = max_priority
        self.pos = (self.pos + 1) % self.buffer_size
        
    def sample(self):
        if len(self.buffer) == self.buffer_size:
            priorities = self.priorities
        else:
            priorities = self.priorities[:self.pos]
        probs = priorities ** self.alpha
        probs /= probs.sum()
        indices = np.random.choice(len(self.buffer), self.batch_size, p=probs)
        samples = [self.buffer[idx] for idx in indices]
        total = len(self.buffer)
        weights = (total * probs[indices]) ** (-self.beta)
        weights /= weights.max()
        self.beta = np.min([1., self.beta + self.beta_increment_per_sampling])
        return indices, samples, weights
    
    def update_priorities(self, batch_indices, batch_priorities):
        for idx, priority in zip(batch_indices, batch_priorities):
            self.priorities[idx] = priority
            
# MADDPG算法类
class MADDPG:
    def __init__(self, state_size, action_size, num_agents):
        self.state_size = state_size
        self.action_size = action_size
        self.num_agents = num_agents
        
        self.actors = [Actor(state_size, action_size) for _ in range(num_agents)]
        self.critics = [Critic((state_size+action_size)*num_agents, 1) for _ in range(num_agents)]
        
        self.target_actors = [Actor(state_size, action_size) for _ in range(num_agents)]
        self.target_critics = [Critic((state_size+action_size)*num_agents, 1) for _ in range(num_agents)]
        
        for i in range(num_agents):
            self.target_actors[i].set_weights(self.actors[i].get_weights())
            self.target_critics[i].set_weights(self.critics[i].get_weights())
            
        self.buffer = PrioritizedReplay(BUFFER_SIZE, BATCH_SIZE)
        
        self.actor_optimizer = [tf.keras.optimizers.Adam(LR_ACTOR) for _ in range(num_agents)]
        self.critic_optimizer = [tf.keras.optimizers.Adam(LR_CRITIC) for _ in range(num_agents)]
        
        self.t_step = 0
        
    def act(self, obs):
        obs = np.array(obs)
        actions = []
        for i in range(self.num_agents):
            action = self.actors[i](obs[i][np.newaxis,:], training=False)
            actions.append(action.numpy())
        actions = np.concatenate(actions, axis=0)
        return actions
    
    def step(self, state, action, reward, next_state, done):
        self.buffer.add(state, action, reward, next_state, done)
        self.t_step = (self.t_step + 1) % UPDATE_EVERY
        if self.t_step == 0 and len(self.buffer.buffer) > BATCH_SIZE:
            for _ in range(NUM_UPDATES):
                indices, samples, weights = self.buffer.sample()
                self.learn(samples, weights)
                self.update_targets()
                self.buffer.update_priorities(indices, weights)
                
    def learn(self, samples, weights):
        states = np.array([sample[0] for sample in samples])
        actions = np.array([sample[1] for sample in samples])
        rewards = np.array([sample[2] for sample in samples])
        next_states = np.array([sample[3] for sample in samples])
        dones = np.array([sample[4] for sample in samples])
        
        for i in range(self.num_agents):
            # 计算Q值
            with tf.GradientTape(persistent=True) as tape:
                target_actions = [self.target_actors[j](next_states[j][np.newaxis,:], training=False) for j in range(self.num_agents)]
                target_actions = np.concatenate(target_actions, axis=0)
                target_qs = self.target_critics[i]((next_states.reshape(-1, self.state_size*self.num_agents), target_actions))
                target_qs = target_qs.numpy().reshape(-1, self.num_agents)
                q_targets = rewards[:,i][:,np.newaxis] + (GAMMA * target_qs * (1 - dones[:,i][:,np.newaxis]))
                critic_qs = self.critics[i]((states.reshape(-1, self.state_size*self.num_agents), actions.reshape(-1, self.action_size*self.num_agents)))
                critic_loss = tf.reduce_mean(weights * (q_targets - critic_qs)**2)
            critic_grads = tape.gradient(critic_loss, self.critics[i].trainable_variables)
            self.critic_optimizer[i].apply_gradients(zip(critic_grads, self.critics[i].trainable_variables))
            
            # 计算Actor梯度
            with tf.GradientTape() as tape:
                actor_actions = [self.actors[j](states[:,j,:], training=False) if j == i else self.actors[j](states[:,j,:], training=True) for j in range(self.num_agents)]
                actor_actions = np.concatenate(actor_actions, axis=0)
                actor_loss = -tf.reduce_mean(self.critics[i]((states.reshape(-1, self.state_size*self.num_agents), actor_actions)))
            actor_grads = tape.gradient(actor_loss, self.actors[i].trainable_variables)
            self.actor_optimizer[i].apply_gradients(zip(actor_grads, self.actors[i].trainable_variables))
            
    def update_targets(self):
        for i in range(self.num_agents):
            self.target_actors[i].set_weights(TAU*np.array(self.actors[i].get_weights())+(1-TAU)*np.array(self.target_actors[i].get_weights()))
            self.target_critics[i].set_weights(TAU*np.array(self.critics[i].get_weights())+(1-TAU)*np.array(self.target_critics[i].get_weights()))

# 环境
env_name = "MPE/3DBall"
env = UnityEnvironment(file_name=env_name)
brain_name = env.brain_names[0]
brain = env.brains[brain_name]
env_info = env.reset()[brain_name]

state_size = env_info.vector_observations.shape[1]
action_size = brain.vector_action_space_size
num_agents = len(env_info.agents)

maddpg = MADDPG(state_size, action_size, num_agents)

scores = []
scores_window = deque(maxlen=100)
for i_episode in range(10000):
    env_info = env.reset()[brain_name]
    obs = env_info.vector_observations
    score = np.zeros(num_agents)
    while True:
        actions = maddpg.act(obs)
        env_info = env.step(actions)[brain_name]
        next_obs = env_info.vector_observations
        rewards = env_info.rewards
        dones = env_info.local_done
        maddpg.step(obs, actions, rewards, next_obs, dones)
        obs = next_obs
        score += rewards
        if np.any(dones):
            break
    scores_window.append(np.max(score))
    scores.append(np.max(score))
    print('\rEpisode {}\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_window)), end="")
    if i_episode % 100 == 0:
        print('\rEpisode {}\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_window)))
    if np.mean(scores_window)>=0.5:
        print('\nEnvironment solved in {:d} episodes!\tAverage Score: {:.2f}'.format(i_episode, np.mean(scores_window)))
        break

env.close()