基于DQN的多智能体强化学习python代码

这里提供一个基于DQN的多智能体强化学习python代码示例：

import numpy as np
import random
import tensorflow as tf
from collections import deque

# 定义DQN类
class DQN(object):
    
    # 初始化DQN网络
    def __init__(self, env):
        self.state_size = env.observation_space.shape[0]
        self.action_size = env.action_space.n
        self.memory = deque(maxlen=2000)
        self.gamma = 0.95
        self.epsilon = 1.0
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.learning_rate = 0.001
        self.model = self._build_model()
    
    # DQN网络的模型结构
    def _build_model(self):
        model = tf.keras.models.Sequential([
            tf.keras.layers.Dense(24, input_dim=self.state_size, activation='relu'),
            tf.keras.layers.Dense(24, activation='relu'),
            tf.keras.layers.Dense(self.action_size, activation='linear')
        ])
        model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(lr=self.learning_rate))
        return model
    
    # 记录记忆
    def remember(self, state, action, reward, next_state, done):
        self.memory.append([state, action, reward, next_state, done])
    
    # 通过DQN网络选择一个动作
    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return random.randrange(self.action_size)
        act_values = self.model.predict(state)
        return np.argmax(act_values[0])
    
    # 训练DQN网络
    def replay(self, batch_size):
        if len(self.memory) < batch_size:
            return
        minibatch = random.sample(self.memory, batch_size)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target = (reward + self.gamma *
                          np.amax(self.model.predict(next_state)[0]))
            target_f = self.model.predict(state)
            target_f[0][action] = target
            self.model.fit(state, target_f, epochs=1, verbose=0)
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay
    
    # 加载保存的DQN网络权重
    def load(self, name):
        self.model.load_weights(name)
    
    # 保存DQN网络权重
    def save(self, name):
        self.model.save_weights(name)

# 定义多智能体强化学习类
class MARL(object):
    
    # 初始化多智能体强化学习
    def __init__(self, env, n_agent):
        self.env = env
        self.n_agent = n_agent
        self.agents = [DQN(env) for _ in range(n_agent)]
        self.batch_size = 32
    
    # 进行多智能体强化学习
    def train(self, max_episode, max_step):
        scores = []
        for e in range(max_episode):
            states = self.env.reset()
            states = np.reshape(states, [1, self.env.observation_space.shape[0] * self.n_agent])
            score = 0
            for t in range(max_step):
                actions = []
                for i in range(self.n_agent):
                    action = self.agents[i].act(states)
                    actions.append(action)
                next_states, rewards, done, _ = self.env.step(actions)
                next_states = np.reshape(next_states, [1, self.env.observation_space.shape[0] * self.n_agent])
                for i in range(self.n_agent):
                    self.agents[i].remember(states, actions[i], rewards[i], next_states, done)
                    self.agents[i].replay(self.batch_size)
                states = next_states
                score += np.sum(rewards)
                if done:
                    break
            scores.append(score)
            print("episode: {}/{}, score: {}".format(e, max_episode, score))

        return scores

该代码仅为示例代码，仅供参考。实际中需要根据具体问题进行适当调整。