深度强化学习简单实战代码

深度强化学习（Deep Reinforcement Learning, DRL）是一种结合了深度神经网络和强化学习的技术，用于解决高维、复杂环境下的决策问题。下面是一个简单的使用Python和Keras库实现的Q-learning（一种常用的强化学习算法）的代码示例，它模拟了一个迷宫环境：

import numpy as np
from keras.models import Sequential
from keras.layers import Dense

# 定义迷宫环境的大小和动作空间
ENV_SIZE = 4
ACTION_SPACE = 4

# 初始化模型
model = Sequential()
model.add(Dense(64, input_shape=(ENV_SIZE*ENV_SIZE,), activation='relu'))
model.add(Dense(ACTION_SPACE, activation='linear'))

# Q值函数初始化
q_table = np.zeros((ENV_SIZE*ENV_SIZE, ACTION_SPACE))

def play_game(state):
    # 获取当前状态对应的Q值向量
    state_values = q_table[state]
    
    # 使用ε-greedy策略选择动作
    if np.random.rand() < EPSILON:
        action = np.random.choice(ACTION_SPACE)
    else:
        action = np.argmax(state_values)

    return action

# 训练过程
for episode in range(MAX_EPISODES):
    # 初始化游戏环境
    state = np.zeros(ENV_SIZE*ENV_SIZE)
    done = False

    while not done:
        action = play_game(state)
        new_state, reward, done = env.step(action)
        
        # 更新Q表
        max_future_q = np.max(q_table[new_state])
        current_q = q_table[state][action]
        new_q = (1 - LEARNING_RATE) * current_q + LEARNING_RATE * (reward + GAMMA * max_future_q)
        q_table[state][action] = new_q
        
        state = new_state

    # 每隔一定步数，降低探索率
    if episode % DECAY_EPSILON == 0 and episode > 0:
        EPSILON -= DECAY_EPSILON_RATE

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