写一个基于python强化学习三维路径规划 QLearning的程序

好的，以下是一个基于Python的QLearning三维路径规划的程序示例：

import numpy as np

# 定义状态空间
states = [(i, j, k) for i in range(10) for j in range(10) for k in range(4)]

# 定义动作空间
actions = [(0, 1), (0, -1), (1, 0), (-1, 0)]

# 定义奖励函数
def reward_func(state, action):
    x, y, z = state
    dx, dy = action
    next_x, next_y = x + dx, y + dy
    if next_x < 0 or next_x >= 10 or next_y < 0 or next_y >= 10:
        return -1  # 撞到边界，给予负奖励
    if next_x == 7 and next_y == 7 and z == 3:
        return 1  # 到达目标位置，给予正奖励
    if next_x == 5 and next_y == 5 and z == 2:
        return 0.5  # 经过特定位置，给予部分奖励
    return 0  # 其他情况，给予零奖励

# 定义Q表
q_table = np.zeros((len(states), len(actions)))

# 初始化参数
learning_rate = 0.1
discount_factor = 0.9
exploration_rate = 0.5
num_episodes = 5000

# 训练模型
for episode in range(num_episodes):
    state = (0, 0, 0)
    while True:
        # 选择动作
        if np.random.uniform(0, 1) < exploration_rate:
            action = actions[np.random.randint(0, len(actions))]
        else:
            q_values = q_table[states.index(state), :]
            action = actions[np.argmax(q_values)]
        # 更新Q表
        next_state = (state[0] + action[0], state[1] + action[1], (state[2] + 1) % 4)
        reward = reward_func(state, action)
        next_q_values = q_table[states.index(next_state), :]
        q_table[states.index(state), actions.index(action)] += learning_rate * (reward + discount_factor * np.max(next_q_values) - q_table[states.index(state), actions.index(action)])
        # 更新状态
        state = next_state
        if state == (7, 7, 3):
            break

# 测试模型
state = (0, 0, 0)
path = [(0, 0)]
while state != (7, 7, 3):
    q_values = q_table[states.index(state), :]
    action = actions[np.argmax(q_values)]
    next_state = (state[0] + action[0], state[1] + action[1], (state[2] + 1) % 4)
    path.append((next_state[0], next_state[1]))
    state = next_state

# 输出结果
print("最短路径为：", path)

这个程序使用QLearning算法来实现三维路径规划，在训练结束后，输出从起点到达目标点的最短路径。在实践中，需要根据具体应用场景进行选择和调整。