强化学习 相关资源

　　最近因为某个不可描述的原因需要迅速用强化学习完成一个小实例，但是之前完全不懂强化学习啊，虽然用了人家的代码但是在找代码的过程中还是发现了很多不错的强化学习资源，决定mark下来以后学习用

【1】如何用简单例子讲解 Q - learning 的具体过程？

　　https://www.zhihu.com/question/26408259

【2】最简单的讲解Q-Learning过程的例子

　　http://mnemstudio.org/path-finding-q-learning-tutorial.htm

　　注：这个网站上还附带了代码，可惜都是用C++，java写的，看不懂，哎，感觉是一个不错的资源网站

　　这篇博客是对应的中文翻译最简单的讲解Q-Learning过程的例子

　　还有人用python按照上述教程完成了复现：

　　https://github.com/JasonQSY/ML-Weekly/blob/master/P5-Reinforcement-Learning/Q-learning/Q-Learning-Get-Started.ipynb

具体代码如下：

import numpy as np
import random

In [44]:

# initial
q = np.zeros([6, 6])
q = np.matrix(q)

r = np.array([[-1, -1, -1, -1, 0, -1], [-1, -1, -1, 0, -1, 100], [-1, -1, -1, 0, -1, -1], [-1, 0, 0, -1, 0, -1], [0, -1, -1, 0, -1, 100], [-1, 0, -1, -1, 0, 100]])
r = np.matrix(r)

gamma = 0.8

In [45]:

# training
for i in range(100):
    # one episode
    state = random.randint(0, 5)
    while (state != 5):
        # choose positive r-value action randomly
        r_pos_action = []
        for action in range(6):
            if r[state, action] >= 0:
                r_pos_action.append(action)
        
        next_state = r_pos_action[random.randint(0, len(r_pos_action) - 1)]
        q[state, next_state] = r[state, next_state] + gamma * q[next_state].max()
        state = next_state

In [46]:

# verify
for i in range(10):
    # one episode
    print("episode: " + str(i + 1))
    
    # random initial state
    state = random.randint(0, 5)
    print("the robot borns in " + str(state) + ".")
    count = 0
    while (state != 5):
        # prevent endless loop
        if count > 20:
            print('fails')
            break
            
        # choose maximal q-value action randomly
        q_max = -100
        for action in range(6):
            if q[state, action] > q_max:
                q_max = q[state, action]
            
        q_max_action = []
        for action in range(6):
            if q[state, action] == q_max:
                q_max_action.append(action)
                
        next_state = q_max_action[random.randint(0, len(q_max_action) - 1)]
        
        print("the robot goes to " + str(next_state) + '.')
        state = next_state
        count = count + 1

【3】这个人的博客有强化学习系列

　　http://www.algorithmdog.com/ml/rl-series

【4】http://blog.csdn.net/u012192662/article/category/6394979

　　粗看感觉写的还可以