【强化学习】python 实现 q-learning 例四（例二改写）

将例二改写成面向对象模式，并加了环境！

不过更新环境的过程中，用到了清屏命令，play()的时候，会有点问题。learn()的时候可以勉强看到:P

0.效果图

1.完整代码

相对于例一，修改的地方：

Agent 五处：states, actions, rewards, get_valid_actions(), get_next_state()

Env    两处：__init__(), update()

import pandas as pd
import random
import time
import pickle
import pathlib
import os

'''
四格迷宫：
---------------
| 入口 |      |
---------------
| 陷阱 | 出口 |
---------------
'''

class Env(object):
    '''环境类'''
    def __init__(self):
        '''初始化'''
        self.env = list('--\n#-')

    def update(self, state, delay=0.1):
        '''更新环境，并打印'''
        env = self.env[:]
        if state > 1:
            state += 1
        env[state] = 'o' # 更新环境
        print('\r{}'.format(''.join(env)), end='')
        time.sleep(delay)
        os.system('cls')

class Agent(object):
    '''个体类'''
    def __init__(self, alpha=0.01, gamma=0.9):
        '''初始化'''
        self.states = range(4)      # 状态集。0, 1, 2, 3 四个状态
        self.actions = list('udlr') # 动作集。上下左右  4个动作
        self.rewards = [0,0,-10,10] # 奖励集。到达位置3（出口）奖励10，位置2（陷阱）奖励-10，其他皆为0

        self.alpha = alpha
        self.gamma = gamma

        self.q_table = pd.DataFrame(data=[[0 for _ in self.actions] for _ in self.states],
                                    index=self.states,
                                    columns=self.actions)

    def save_policy(self):
        '''保存Q table'''
        with open('q_table.pickle', 'wb') as f:
            # Pickle the 'data' dictionary using the highest protocol available.
            pickle.dump(self.q_table, f, pickle.HIGHEST_PROTOCOL)

    def load_policy(self):
        '''导入Q table'''
        with open('q_table.pickle', 'rb') as f:
            self.q_table = pickle.load(f)

    def choose_action(self, state, epsilon=0.8):
        '''选择相应的动作。根据当前状态，随机或贪婪，按照参数epsilon'''
        if (random.uniform(0,1) > epsilon) or ((self.q_table.ix[state] == 0).all()):  # 探索
            action = random.choice(self.get_valid_actions(state))
        else:
            action = self.q_table.ix[state].idxmax() # 利用（贪婪）
        return action

    def get_q_values(self, state):
        '''取状态state的所有Q value'''
        q_values = self.q_table.ix[state, self.get_valid_actions(state)]
        return q_values

    def update_q_value(self, state, action, next_state_reward, next_state_q_values):
        '''更新Q value，根据贝尔曼方程'''
        self.q_table.ix[state, action] += self.alpha * (next_state_reward + self.gamma * next_state_q_values.max() - self.q_table.ix[state, action])

    def get_valid_actions(self, state):
        '''取当前状态下所有的合法动作'''
        valid_actions = set(self.actions)
        if state % 2 == 1:               # 最后一列，则
            valid_actions -= set(['r'])  # 无向右的动作
        if state % 2 == 0:               # 最前一列，则
            valid_actions -= set(['l'])  # 无向左
        if state // 2 == 1:              # 最后一行，则
            valid_actions -= set(['d'])  # 无向下
        if state // 2 == 0:              # 最前一行，则
            valid_actions -= set(['u'])  # 无向上
        return list(valid_actions)

    def get_next_state(self, state, action):
        '''对状态执行动作后，得到下一状态'''
        #u,d,l,r,n = -2,+2,-1,+1,0
        if state % 2 != 1 and action == 'r':    # 除最后一列，皆可向右(+1)
            next_state = state + 1
        elif state % 2 != 0 and action == 'l':  # 除最前一列，皆可向左(-1)
            next_state = state -1
        elif state // 2 != 1 and action == 'd': # 除最后一行，皆可向下(+2)
            next_state = state + 2
        elif state // 2 != 0 and action == 'u': # 除最前一行，皆可向上(-2)
            next_state = state - 2
        else:
            next_state = state
        return next_state

    def learn(self, env=None, episode=1000, epsilon=0.8):
        '''q-learning算法'''
        print('Agent is learning...')
        for _ in range(episode):
            current_state = self.states[0]

            if env is not None: # 若提供了环境，则更新之！
                env.update(current_state)

            while current_state != self.states[-1]:
                current_action = self.choose_action(current_state, epsilon) # 按一定概率，随机或贪婪地选择
                next_state = self.get_next_state(current_state, current_action)
                next_state_reward = self.rewards[next_state]
                next_state_q_values = self.get_q_values(next_state)
                self.update_q_value(current_state, current_action, next_state_reward, next_state_q_values)
                current_state = next_state

                if env is not None: # 若提供了环境，则更新之！
                    env.update(current_state)
        print('\nok')

    def play(self, env=None, delay=0.5):
        '''玩游戏，使用策略'''
        assert env != None, 'Env must be not None!'

        if pathlib.Path("q_table.pickle").exists():
            self.load_policy()
        else:
            print("I need to learn before playing this game.")
            self.learn(env, 13)
            self.save_policy()

        print('Agent is playing...')
        current_state = self.states[0]
        env.update(current_state, delay)
        while current_state != self.states[-1]:
            current_action = self.choose_action(current_state, 1.) # 1., 不随机
            next_state = self.get_next_state(current_state, current_action)
            current_state = next_state
            env.update(current_state, delay)
        print('\nCongratulations, Agent got it!')

if __name__ == '__main__':
    env = Env()     # 环境
    agent = Agent() # 个体
    agent.learn(env, episode=25, epsilon=0.6) # 先学
    #agent.save_policy() # 保存所学
    #agent.load_policy() # 导入所学
    #agent.play(env)              # 再玩