#!/usr/bin/env python
# coding=utf-8
'''
Author: John
Email: johnjim0816@gmail.com
Date: 2020-09-11 23:03:00
LastEditor: John
LastEditTime: 2020-10-07 20:48:29
Discription: 
Environment: 
'''
#   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.


import numpy as np
import math

class QLearning(object):
    def __init__(self,
                 obs_dim,
                 action_dim,
                 learning_rate=0.01,
                 gamma=0.9,
                 epsilon_start=0.9,epsilon_end=0.1,epsilon_decay=200):
        self.action_dim = action_dim  # 动作维度，有几个动作可选
        self.lr = learning_rate  # 学习率
        self.gamma = gamma  # reward 的衰减率
        self.epsilon = 0  # 按一定概率随机选动作，即 e-greedy 策略, 并且epsilon逐渐衰减
        self.sample_count = 0 #  epsilon随训练的也就是采样次数逐渐衰减，所以需要计数
        self.epsilon_start = epsilon_start
        self.epsilon_end = epsilon_end
        self.epsilon_decay= epsilon_decay
        self.Q_table = np.zeros((obs_dim, action_dim)) # Q表

    def sample(self, obs):
        '''根据输入观测值，采样输出的动作值，带探索，训练模型时使用
        '''
        self.sample_count += 1
        self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
            math.exp(-1. * self.sample_count / self.epsilon_decay)
        if np.random.uniform(0, 1) > self.epsilon:  # 随机选取0-1之间的值，如果大于epsilon就按照贪心策略选取action，否则随机选取
            action = self.predict(obs)
        else:
            action = np.random.choice(self.action_dim)  #有一定概率随机探索选取一个动作
        return action
    def predict(self, obs):
        '''根据输入观测值，采样输出的动作值，带探索，测试模型时使用
        '''
        Q_list = self.Q_table[obs, :]
        Q_max = np.max(Q_list)
        action_list = np.where(Q_list == Q_max)[0]  
        action = np.random.choice(action_list) # Q_max可能对应多个 action ，可以随机抽取一个
        return action

    def learn(self, obs, action, reward, next_obs, done):
        '''学习方法(off-policy)，也就是更新Q-table的方法
        Args:
            obs [type]: 交互前的obs, s_t
            action [type]: 本次交互选择的action, a_t
            reward [type]: 本次动作获得的奖励r
            next_obs [type]: 本次交互后的obs, s_t+1
            done function:  episode是否结束
        '''
        Q_predict = self.Q_table[obs, action]
        if done:
            Q_target = reward  # 没有下一个状态了
        else:
            Q_target = reward + self.gamma * np.max(
                self.Q_table[next_obs, :])  # Q_table-learning
        self.Q_table[obs, action] += self.lr * (Q_target - Q_predict)  # 修正q

    def save(self):
        '''把 Q表格 的数据保存到文件中
        '''
        npy_file = './Q_table.npy'
        np.save(npy_file, self.Q_table)
        print(npy_file + ' saved.')
    def load(self, npy_file='./Q_table.npy'):
        '''从文件中读取数据到 Q表格
        '''
        self.Q_table = np.load(npy_file)
        print(npy_file + 'loaded.')