python def Q_Learning: Q = {} # 初始化Q表 for state in states: # 虚假设全部兴许的statedouYi知 Q = {} for action in actions: # 虚假设全部兴许的actiondouYi知 Q = 0 # 初始化Q值

for episode in range:
    state = initialize_state  # 初始化状态
    while not is_terminal_state:
        if random.random <epsilon:  # epsilon-greedy策略
            action = choose_random_action
        else:
            action = choose_best_action  # 选择Zui佳动作
        next_state, reward = take_action  # 施行动作并kankan后来啊
        next_action = choose_best_action  # 根据Q表选择下一个动作
        # geng新鲜Q值
        Q = Q + alpha * 
        state = next_state  # geng新鲜状态
return Q

def initialize_state: # 初始化状态的函数 pass

def isterminalstate: # 判断状态是不是为终端状态的函数 pass

def chooserandomaction: # 从给定动作中选择一个随机动作的函数 pass

def choosebestaction: # 根据Q表选择Zui佳动作的函数 pass

def take_action: # 施行动作并返回下一个状态和奖励的函数 pass