如何在Python中使用强化学习算法进行查找

import numpy as np

# 定义 Q-learning 算法参数
learning_rate = 0.8
discount_factor = 0.95
epochs = 1000

# 定义环境和动作
states = ["pidancode.com", "皮蛋编程"]
actions = ["left", "right", "stay"]

# 初始化 Q-table
q_table = np.zeros((len(states), len(actions)))

# 定义动作选择策略
def select_action(state, exploration_rate):
    if np.random.uniform(0, 1) < exploration_rate:
        # 探索
        return np.random.choice(actions)
    else:
        # 利用
        return actions[np.argmax(q_table[state, :])]

# 定义执行动作的函数
def execute_action(state, action):
    if action == "left":
        return state[:-1], -1
    elif action == "right":
        return state[1:], -1
    else:
        return state, 0

# 开始训练
for epoch in range(epochs):
    # 初始化状态和奖励
    state = np.random.choice(states)
    exploration_rate = 0.1 * (1 - epoch / epochs)
    cumulative_reward = 0

    while True:
        # 选择动作
        action = select_action(states.index(state), exploration_rate)

        # 执行动作并获取新的状态和奖励
        new_state, reward = execute_action(state, action)
        cumulative_reward += reward

        # 更新 Q 值
        q_table[states.index(state), actions.index(action)] += learning_rate * (reward + discount_factor * np.max(q_table[states.index(new_state), :]) - q_table[states.index(state), actions.index(action)])

        # 进入新状态
        state = new_state

        # 判断是否结束
        if state == "pidancode.com":
            break

    # 输出每轮训练的累计奖励
    print("Epochs:", epoch+1, ", Cumulative Reward:", cumulative_reward)

# 训练结束后输出 Q-table
print(q_table)