强化学习------Qlearning算法-编程知识

简介

Q learning 算法是一种value-based的强化学习算法，Q是quality的缩写，Q函数 Q(state，action)表示在状态state下执行动作action的quality，也就是能获得的Q value是多少。算法的目标是最大化Q值，通过在状态state下所有可能的动作中选择最好的动作来达到最大化期望reward。

Q learning算法使用Q table来记录不同状态下不同动作的预估Q值。在探索环境之前，Q table会被随机初始化，当agent在环境中探索的时候，它会用贝尔曼方程（ballman equation）来迭代更新Q(s,a)，随着迭代次数的增多，agent会对环境越来越了解，Q 函数也能被拟合得越来越好，直到收敛或者到达设定的迭代结束次数。
伪代码如下：
在这里插入图片描述
整个算法就是一直不断更新 Q table 里的值, 然后再根据新的值来判断要在某个 state 采取怎样的 action. Qlearning 是一个 off-policy 的算法, 因为里面的 max action 让 Q table 的更新可以不基于正在经历的经验(可以是现在学习着很久以前的经验,甚至是学习他人的经验). 不过这一次的例子, 我们没有运用到 off-policy, 而是把 Qlearning 用在了 on-policy 上, 也就是现学现卖, 将现在经历的直接当场学习并运用. On-policy 和 off-policy 的差别我们会在之后的 [Deep Q network (off-policy)] 学习中见识到. 而之后的教程也会讲到一个 on-policy (Sarsa) 的形式, 我们之后再对比.

算法实战

我们使用openAI的gym中的CliffWalking-v0作为环境

#!/usr/bin/env python 
# -*- coding:utf-8 -*-
import numpy as np
import gym
import time
import gridworld#Sarsa算法
class QLearning():def __init__(self,num_states,num_actions,e_greed=0.1,lr=0.9,gamma=0.8):#建立Q表格self.Q = np.zeros((num_states,num_actions))self.e_greed = e_greed   #探索概率self.num_states = num_statesself.num_actions = num_actionsself.lr = lr   #学习率self.gamma = gamma #折扣因子def predict(self,state):"""通过当前状态预测下一个动作:param state::return:"""#获取当前状态的所有动作的切片Q_list = self.Q[state,:]#随机选取其中最大值中的某一个（防止存在多个最大值时，总是选最前面的问题）action = np.random.choice(np.flatnonzero(Q_list == Q_list.max()))return  actiondef action(self,state):"""选取动作:param state::return:"""#探索,随机选择一个动作if np.random.uniform(0,1) < self.e_greed:action = np.random.choice(self.num_actions)else:   #直接选取最大Q值的动作action = self.predict(state)return actiondef learn(self,state,action,reward,next_state,done):cur_Q = self.Q[state,action]# 当游戏结束时，不存在next_action和next_statetarget_Q = reward + (1-float(done))*self.gamma*self.Q[next_state,:].max()self.Q[state,action] += self.lr*(target_Q - cur_Q)#训练
def train_episode(env,agent,is_render):total_reward = 0#初始化环境state,_ = env.reset()while True:action = agent.action(state)#执行动作返回结果next_state,reward,done,_,_ = env.step(action)#更新参数agent.learn(state,action,reward,next_state,done)#循环执行state = next_statetotal_reward += rewardif is_render:env.render()if done:breakreturn  total_reward
#测试
def test_episode(env,agent,is_render=False):total_reward = 0# 初始化环境state,_ = env.reset()while True:action = agent.predict(state)next_state, reward, done, _,_ = env.step(action)state = next_statetotal_reward += rewardenv.render()time.sleep(0.5)if done:breakreturn total_reward
#训练
def train(env,episodes=500,lr=0.1,gamma=0.9,e_greed=0.1):agent = QLearning(num_states = env.observation_space.n,num_actions = env.action_space.n,lr = lr,gamma = gamma,e_greed = e_greed)is_render = False#先训练episodes次for e in range(episodes):ep_reward = train_episode(env,agent,is_render)print('Episode %s : reward= %.1f'%(e,ep_reward))#每执行50轮就显示一次if e%50 == 0:is_render = Trueelse:is_render = False#训练结束后，我i们测试模型test_reward = test_episode(env,agent)print('test_reward= %.1f' % (test_reward))if __name__ == '__main__':env = gym.make("CliffWalking-v0")env = gridworld.CliffWalkingWapper(env)train(env)

运行效果

在这里插入图片描述

另附工具类

用于可视化游戏界面

#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.# -*- coding: utf-8 -*-import gym
import turtle
import numpy as np# turtle tutorial : https://docs.python.org/3.3/library/turtle.htmldef GridWorld(gridmap=None, is_slippery=False):if gridmap is None:gridmap = ['SFFF', 'FHFH', 'FFFH', 'HFFG']env = gym.make("FrozenLake-v0", desc=gridmap, is_slippery=False)env = FrozenLakeWapper(env)return envclass FrozenLakeWapper(gym.Wrapper):def __init__(self, env):gym.Wrapper.__init__(self, env)self.max_y = env.desc.shape[0]self.max_x = env.desc.shape[1]self.t = Noneself.unit = 50def draw_box(self, x, y, fillcolor='', line_color='gray'):self.t.up()self.t.goto(x * self.unit, y * self.unit)self.t.color(line_color)self.t.fillcolor(fillcolor)self.t.setheading(90)self.t.down()self.t.begin_fill()for _ in range(4):self.t.forward(self.unit)self.t.right(90)self.t.end_fill()def move_player(self, x, y):self.t.up()self.t.setheading(90)self.t.fillcolor('red')self.t.goto((x + 0.5) * self.unit, (y + 0.5) * self.unit)def render(self):if self.t == None:self.t = turtle.Turtle()self.wn = turtle.Screen()self.wn.setup(self.unit * self.max_x + 100,self.unit * self.max_y + 100)self.wn.setworldcoordinates(0, 0, self.unit * self.max_x,self.unit * self.max_y)self.t.shape('circle')self.t.width(2)self.t.speed(0)self.t.color('gray')for i in range(self.desc.shape[0]):for j in range(self.desc.shape[1]):x = jy = self.max_y - 1 - iif self.desc[i][j] == b'S':  # Startself.draw_box(x, y, 'white')elif self.desc[i][j] == b'F':  # Frozen iceself.draw_box(x, y, 'white')elif self.desc[i][j] == b'G':  # Goalself.draw_box(x, y, 'yellow')elif self.desc[i][j] == b'H':  # Holeself.draw_box(x, y, 'black')else:self.draw_box(x, y, 'white')self.t.shape('turtle')x_pos = self.s % self.max_xy_pos = self.max_y - 1 - int(self.s / self.max_x)self.move_player(x_pos, y_pos)class CliffWalkingWapper(gym.Wrapper):def __init__(self, env):gym.Wrapper.__init__(self, env)self.t = Noneself.unit = 50self.max_x = 12self.max_y = 4def draw_x_line(self, y, x0, x1, color='gray'):assert x1 > x0self.t.color(color)self.t.setheading(0)self.t.up()self.t.goto(x0, y)self.t.down()self.t.forward(x1 - x0)def draw_y_line(self, x, y0, y1, color='gray'):assert y1 > y0self.t.color(color)self.t.setheading(90)self.t.up()self.t.goto(x, y0)self.t.down()self.t.forward(y1 - y0)def draw_box(self, x, y, fillcolor='', line_color='gray'):self.t.up()self.t.goto(x * self.unit, y * self.unit)self.t.color(line_color)self.t.fillcolor(fillcolor)self.t.setheading(90)self.t.down()self.t.begin_fill()for i in range(4):self.t.forward(self.unit)self.t.right(90)self.t.end_fill()def move_player(self, x, y):self.t.up()self.t.setheading(90)self.t.fillcolor('red')self.t.goto((x + 0.5) * self.unit, (y + 0.5) * self.unit)def render(self):if self.t == None:self.t = turtle.Turtle()self.wn = turtle.Screen()self.wn.setup(self.unit * self.max_x + 100,self.unit * self.max_y + 100)self.wn.setworldcoordinates(0, 0, self.unit * self.max_x,self.unit * self.max_y)self.t.shape('circle')self.t.width(2)self.t.speed(0)self.t.color('gray')for _ in range(2):self.t.forward(self.max_x * self.unit)self.t.left(90)self.t.forward(self.max_y * self.unit)self.t.left(90)for i in range(1, self.max_y):self.draw_x_line(y=i * self.unit, x0=0, x1=self.max_x * self.unit)for i in range(1, self.max_x):self.draw_y_line(x=i * self.unit, y0=0, y1=self.max_y * self.unit)for i in range(1, self.max_x - 1):self.draw_box(i, 0, 'black')self.draw_box(self.max_x - 1, 0, 'yellow')self.t.shape('turtle')x_pos = self.s % self.max_xy_pos = self.max_y - 1 - int(self.s / self.max_x)self.move_player(x_pos, y_pos)if __name__ == '__main__':# 环境1：FrozenLake, 可以配置冰面是否是滑的# 0 left, 1 down, 2 right, 3 upenv = gym.make("FrozenLake-v0", is_slippery=False)env = FrozenLakeWapper(env)# 环境2：CliffWalking, 悬崖环境# env = gym.make("CliffWalking-v0")  # 0 up, 1 right, 2 down, 3 left# env = CliffWalkingWapper(env)# 环境3：自定义格子世界，可以配置地图, S为出发点Start, F为平地Floor, H为洞Hole, G为出口目标Goal# gridmap = [#         'SFFF',#         'FHFF',#         'FFFF',#         'HFGF' ]# env = GridWorld(gridmap)env.reset()for step in range(10):action = np.random.randint(0, 4)obs, reward, done, info = env.step(action)print('step {}: action {}, obs {}, reward {}, done {}, info {}'.format(\step, action, obs, reward, done, info))env.render()  # 渲染一帧图像