#!/usr/bin/env python
# coding=utf-8
'''
Author: John
Email: johnjim0816@gmail.com
Date: 2020-09-11 23:03:00
LastEditor: John
LastEditTime: 2021-09-15 13:18:37
Discription: use defaultdict to define Q table
Environment: 
'''
import numpy as np
import math
import torch
from collections import defaultdict

class QLearning(object):
    def __init__(self,state_dim,
                 action_dim,cfg):
        self.action_dim = action_dim  # dimension of acgtion
        self.lr = cfg.lr  # learning rate
        self.gamma = cfg.gamma  
        self.epsilon = 0 
        self.sample_count = 0  
        self.epsilon_start = cfg.epsilon_start
        self.epsilon_end = cfg.epsilon_end
        self.epsilon_decay = cfg.epsilon_decay
        self.Q_table  = defaultdict(lambda: np.zeros(action_dim)) # A nested dictionary that maps state -> (action -> action-value)
    def choose_action(self, state):
        self.sample_count += 1
        self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
            math.exp(-1. * self.sample_count / self.epsilon_decay) # epsilon是会递减的，这里选择指数递减
        # e-greedy 策略
        if np.random.uniform(0, 1) > self.epsilon:
            action = np.argmax(self.Q_table[str(state)]) # 选择Q(s,a)最大对应的动作
        else:
            action = np.random.choice(self.action_dim) # 随机选择动作
        return action
    def predict(self,state):
        action = np.argmax(self.Q_table[str(state)])
        return action
    def update(self, state, action, reward, next_state, done):
        Q_predict = self.Q_table[str(state)][action] 
        if done: # 终止状态
            Q_target = reward  
        else:
            Q_target = reward + self.gamma * np.max(self.Q_table[str(next_state)]) 
        self.Q_table[str(state)][action] += self.lr * (Q_target - Q_predict)
    def save(self,path):
        import dill
        torch.save(
            obj=self.Q_table,
            f=path+"Qleaning_model.pkl",
            pickle_module=dill
        )
        print("保存模型成功！")
    def load(self, path):
        import dill
        self.Q_table =torch.load(f=path+'Qleaning_model.pkl',pickle_module=dill)
        print("加载模型成功！")