import pandas as pd

import random

import time

import pickle

import pathlib

'''

-o---T

# T 就是宝藏的位置, o 是探索者的位置

作者：hhh5460
时间：20181218
地点：Tai Zi Miao

'''

class Env(object):

    '''环境类'''

    def __init__(self):

        '''初始化'''

        self.env = list('-----T')

    def update(self, state, delay=0.1):

        '''更新环境，并打印'''

        env = self.env[:]

        env[state] = 'o' # 更新环境

        print('\r{}'.format(''.join(env)), end='')

        time.sleep(delay)

class Agent(object):

    '''个体类'''

    def __init__(self, alpha=0.01, gamma=0.9):

        '''初始化'''

        self.states = range(6)

        self.actions = ['left', 'right']

        self.rewards = [0,0,0,0,0,1]

        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 == self.states[-1]:             # 最后一个状态（位置），则

            valid_actions -= set(['right']) # 不能向右

        if state == self.states[0]:              # 最前一个状态（位置），则

            valid_actions -= set(['left'])  # 不能向左

        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'''

        # l,r,n = -1,+1,0

        if action == 'right' and state != self.states[-1]: # 除非最后一个状态（位置），向右就+1

            next_state = state + 1

        elif action == 'left' and state != self.states[0]: # 除非最前一个状态（位置），向左就-1

            next_state = state -1

        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=13) # 先学

    #agent.save_policy() # 保存所学

    #agent.load_policy() # 导入所学

    agent.play(env)              # 再玩