主要是讲述一下自己学习Qlearning和Sarsa的感悟

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前言

自从北京学习回到成都，一个暑假虽然”书本上“的知识没有学到什么，但是确实让身为本科生的自己眼界开阔了很多，也更加的明确了自己的目标。强化学习这一个领域之前也是仅仅知识了解大概的算法流程，知其然，不知其所以然。直到现在我也觉得其和遗传学习很相似，难道他们之间是包含与被包含的关系？（需要再继续学习才能去了解了）
本篇口水话主要是讲述一下Qlearning的算法特点，以及其与Sarsa之间的关系和不同之处，希望能给大家带来帮助。算法示例根据莫凡python的程序改：
https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow

Qlearning算法简单示例

Qlearning算法的流程：

1、建立qtable,设置学习率alpha，贪心指数greedy，记忆程度gamma，初始化状态state
2、根据当前的state，选择action（greedy主要在选择action起到作用，主要是帮助算法跳出局部最优）
3、根据选择的action，计算新的state_，和奖励reward
4、在qtable中 q_old = qtable[state,action] 和 q_new = reward + gammaqtable[state_,:],max()
5、更新qtable，qtable[state,action] += alpha(q_new-q_old）
6、state = state_
7、循环步骤2-6

#Qlearning算法

import pandas as pd
import numpy as np
import time
import sys
class Rl:
    def __init__(self):
        self.n_state = 6     #------T
        self.n_epoch = 15
        self.action = ['left','right']
        self.alpha = 0.1
        self.greedy = 0.9
        self.gamma = 0.9
        self.fresh = 0.1
        # np.random.seed(0)

    def build_qtable(self):
        q_table = pd.DataFrame(np.zeros((self.n_state,len(self.action))),columns=self.action)
        # print(q_table)
        return q_table

    def chose_action(self,state,q_table):
        action = q_table.iloc[state,:]
        if np.random.rand()>self.greedy or action.all()==0:
            idx_name = np.random.choice(action.index)
            # print('\r'+idx_name,end='')
        else:
            idx_name = action.idxmax()
        return idx_name

    def get_env_feedback(self,state,action):
        if action == 'right':
            if state == self.n_state-1:
                reward = 1
                state_ = 'terminal'
            else:
                reward = 0
                state_ = state + 1
        else:
            if state == 0:
                state_ = 0
                reward = 0
            else:
                state_ = state - 1
                reward = 0
        return state_,reward

    def update_env(self,state,episode,n_step):
        env = ['-']*self.n_state+['T']
        if state == 'terminal':
            result = '第{0}次，总共走了{1}次'.format(episode,n_step)
            print(result)
        else:
            env[state] = 'o'
            print('\r'+''.join(env),end='')
            time.sleep(0.1)

    def train(self):
        q_table = self.build_qtable()
        for episode in range(self.n_epoch):
            n_step = 0
            state = 0
            is_compelete = False
            self.update_env(state,episode,n_step)
            while not is_compelete:
                action = self.chose_action(state,q_table)
                state_,reward = self.get_env_feedback(state,action)
                q_predict = q_table.loc[state,action]
                if state_ != 'terminal':
                    q_real = reward + self.gamma*q_table.iloc[state_,:].max()
                else:
                    q_real = reward
                    is_compelete = True

                q_table.loc[state,action] += self.alpha*(q_real-q_predict)
                n_step += 1
                state = state_
                self.update_env(state,episode,n_step)
                # print(q_table)

        return q_table

rl = Rl()
rl.train()

Sarsa算法简单示例

Qlearning算法的流程：

1、建立qtable,设置学习率alpha，贪心指数greedy，记忆程度gamma，初始化状态state
2、根据当前的state，选择action（greedy主要在选择action起到作用，主要是帮助算法跳出局部最优）
3、根据选择的action，计算新的state_，和奖励reward
4、在qtable中 q_old = qtable[state,action] 和 q_new = reward + gammaqtable[state_,action]
5、更新qtable，qtable[state,action] += alpha(q_new-q_old），state = state_
6、state = state_，下一次循环执行的action就是此次循环的action，下一次循环选出的action_作为下下次执行
7、循环步骤2-6

#Sarsa算法
import pandas as pd
import time
import numpy as np

class Rl:
    def __init__(self):
        self.n_state = 6     #------T
        self.n_epoch = 20
        self.action = ['left','right']
        self.alpha = 0.1
        self.greedy = 0.9
        self.gamma = 0.9
        self.fresh = 0.01
        # np.random.seed(0)

    def build_qtable(self):
        q_table = pd.DataFrame(np.zeros((self.n_state,len(self.action))),columns=self.action)
        # print(q_table)
        return q_table

    def chose_action(self,state,q_table,action_):
        action = q_table.iloc[state,:]
        if np.random.rand()>self.greedy or action.all()==0:
            idx_name = np.random.choice(action.index)
            # print('\r'+idx_name,end='')
        else:
            idx_name = action.idxmax()
        if action_ == None:
            return idx_name,idx_name
        else:
            return action_, idx_name


    def get_env_feedback(self,state,action):
        if action == 'right':
            if state == self.n_state-1:
                reward = 1
                state_ = 'terminal'
            else:
                reward = 0
                state_ = state + 1
        else:
            if state == 0:
                state_ = 0
                reward = 0
            else:
                state_ = state - 1
                reward = 0
        return state_,reward

    def update_env(self,state,episode,n_step):
        env = ['-']*self.n_state+['T']
        if state == 'terminal':
            result = '第{0}次，总共走了{1}次'.format(episode,n_step)
            print(result)
        else:
            env[state] = 'o'
            print('\r'+''.join(env),end='')
            time.sleep(0.1)

    def train(self):
        q_table = self.build_qtable()
        for episode in range(self.n_epoch):
            n_step = 0
            state = 0
            is_compelete = False
            action_ = None
            self.update_env(state,episode,n_step)
            while not is_compelete:
                action,action_ = self.chose_action(state,q_table,action_)
                state_,reward = self.get_env_feedback(state,action)
                q_predict = q_table.loc[state,action_]
                if state_ != 'terminal':
                    q_real = reward + self.gamma*q_table.loc[state_,action_]
                else:
                    q_real = reward
                    is_compelete = True

                q_table.loc[state,action_] += self.alpha*(q_real-q_predict)

                n_step += 1
                state = state_
                self.update_env(state,episode,n_step)
                # print(q_table)

        return q_table

rl = Rl()
rl.train()

Qlearning执行结果

Sarsa程序的执行效果

虽然上述的程序执行结果具有随机性，但是大概是可以看到Qlearning收敛的速度比较快，Sarsa收敛可能需要更多的时间，但是Sarsa相比较于Qlearing更加不容易”犯错“。

总结

Qlearning和Sarsa算法都是通过建立qtable来做决策，其区别主要是在action执行部分，Qlearning会模拟执行，但是在计算q_new时并不一定执行模拟的action，而是选择qtable[state_,:].max()对应的action。Sarsa则根据挑选出来的action，计算q_new时执行挑选出来的action。

甘愿为理想“头破血流”