dqn算法解决不同问题的实例代码实现

DQN（Deep Q-Network）是深度强化学习的一种算法，用于解决不同问题的实例代码实现如下所示：

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Dense

# 创建DQN agent类
class DQNAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        
        self.memory = [] # 记忆回放缓存
        self.gamma = 0.95 # discount rate
        self.epsilon = 1.0 # 探索和开发的权衡
        self.epsilon_decay = 0.995 # epsilon衰减率
        self.epsilon_min = 0.01 # epsilon的最小值
        self.learning_rate = 0.001 # 学习率
        
        self.model = self.build_model() # 创建神经网络模型
        
    # 创建DQN模型
    def build_model(self):
        model = tf.keras.Sequential()
        model.add(Dense(24, input_dim=self.state_size, activation='relu'))
        model.add(Dense(24, activation='relu'))
        model.add(Dense(self.action_size, activation='linear'))
        model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(lr=self.learning_rate))
        return model
    
    # 储存记忆
    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))
    
    # 根据当前状态选择动作
    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return np.random.choice(self.action_size)
        act_values = self.model.predict(state)
        return np.argmax(act_values[0])
    
    # 训练模型
    def train(self, batch_size):
        minibatch = np.random.choice(len(self.memory), batch_size, replace=False)
        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
    
    # 加载模型
    def load(self, name):
        self.model.load_weights(name)
    
    # 保存模型
    def save(self, name):
        self.model.save_weights(name)

# 示例代码实现
env = gym.make('CartPole-v0') # 创建环境
state_size = env.observation_space.shape[0]
action_size = env.action_space.n

agent = DQNAgent(state_size, action_size) # 创建DQN agent对象

batch_size = 32
num_episodes = 1000
for episode in range(num_episodes):
    state = env.reset()
    state = np.reshape(state, [1, state_size])
    done = False
    for time in range(500):
        # env.render() # 可选，用于显示游戏界面
        action = agent.act(state) # 根据当前状态选择动作
        next_state, reward, done, _ = env.step(action)
        reward = reward if not done else -10
        next_state = np.reshape(next_state, [1, state_size])
        agent.remember(state, action, reward, next_state, done) # 储存记忆
        state = next_state
        if done:
            print("episode: {}/{}, score: {}, e: {:.2}".format(episode, num_episodes, time, agent.epsilon))
            break
        if len(agent.memory) > batch_size:
            agent.train(batch_size) # 模型训练

# 保存模型
agent.save("dqn_model.h5")

以上是DQN算法解决问题的一个简单实例代码实现，其中使用`gym`库创建了一个`CartPole-v0`的环境。在每个episode中，根据当前状态选择动作，与环境交互并获得奖励，然后将这些信息存储在记忆中，最后使用记忆进行训练来更新DQN模型。模型训练过程中还使用了经验回放（Experience Replay）和目标网络（Target Network）的技术来提高学习效果。