python实现q-learning迷宫

好的，下面是Python实现Q-learning迷宫的代码：

import numpy as np
import random

# 定义迷宫结构
maze = np.array([
    [0, -1, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, -1, 0, -1, -1, -1, 0, -1, -1, 0],
    [0, -1, 0, -1, 0, -1, 0, -1, 0, 0],
    [0, -1, -1, 0, 0, -1, 0, -1, 0, 0],
    [0, 0, 0, -1, -1, -1, 0, -1, -1, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, -1, 0],
    [0, -1, -1, -1, -1, -1, -1, -1, -1, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
])

# 定义状态数量
states = maze.shape[0] * maze.shape[1]

# 定义动作数量
actions = 4

# 定义学习率
alpha = 0.1

# 定义折扣因子
gamma = 0.9

# 定义探索率
epsilon = 0.1

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

# 定义状态转换函数
def state_transition(state, action):
    row = state // maze.shape[1]
    col = state % maze.shape[1]
    
    if action == 0: # 上
        if row == 0 or maze[row-1, col] == -1:
            return state
        else:
            return state - maze.shape[1]
    elif action == 1: # 下
        if row == maze.shape[0]-1 or maze[row+1, col] == -1:
            return state
        else:
            return state + maze.shape[1]
    elif action == 2: # 左
        if col == 0 or maze[row, col-1] == -1:
            return state
        else:
            return state - 1
    elif action == 3: # 右
        if col == maze.shape[1]-1 or maze[row, col+1] == -1:
            return state
        else:
            return state + 1

# 定义选择动作函数
def choose_action(state):
    if random.uniform(0, 1) < epsilon:
        return random.randint(0, actions-1)
    else:
        return np.argmax(q_table[state])

# 定义Q-learning算法函数
def q_learning():
    for i in range(10000):
        state = random.randint(0, states-1)
        while state // maze.shape[1] != 0 or state % maze.shape[1] == 0 or state % maze.shape[1] == maze.shape[1]-1:
            state = random.randint(0, states-1)
        for j in range(100):
            action = choose_action(state)
            next_state = state_transition(state, action)
            reward = -1 if next_state // maze.shape[1] == maze.shape[0]-1 and next_state % maze.shape[1] != 0 and next_state % maze.shape[1] != maze.shape[1]-1 else 0
            q_table[state, action] = q_table[state, action] + alpha * (reward + gamma * np.max(q_table[next_state]) - q_table[state, action])
            state = next_state

# 执行Q-learning算法
q_learning()

# 输出Q值表和策略
print(q_table)
policy = np.zeros((maze.shape[0], maze.shape[1]))
for i in range(maze.shape[0]):
    for j in range(maze.shape[1]):
        if maze[i, j] != -1:
            policy[i, j] = np.argmax(q_table[i*maze.shape[1]+j])
print(policy)

在这个代码中，我们首先定义了一个迷宫的结构，然后定义了状态数量、动作数量、学习率、折扣因子和探索率等参数。接着定义了状态转换函数、选择动作函数和Q-learning算法函数。在Q-learning算法函数中，我们使用了10000次迭代和100步每次迭代的方式进行训练，对于每个状态和动作组合，我们都更新了对应的Q值。最后输出了Q值表和策略。