#!/usr/bin/env python
# coding=utf-8
'''
Author: John
Email: johnjim0816@gmail.com
Date: 2020-09-11 23:03:00
LastEditor: John
LastEditTime: 2022-06-21 19:36:05
Discription: 
Environment: 
'''
import sys
import os
curr_path = os.path.dirname(os.path.abspath(__file__))  # 当前文件所在绝对路径
parent_path = os.path.dirname(curr_path)  # 父路径
sys.path.append(parent_path)  # 添加路径到系统路径

import gym
import torch
import datetime

from env.gridworld_env import CliffWalkingWapper
from qlearning import QLearning
from common.utils import plot_rewards
from common.utils import save_results,make_dir

curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # 获取当前时间
class Config:
    '''超参数
    '''

    def __init__(self):
        ################################## 环境超参数 ###################################
        self.algo_name = 'Q-learning'  # 算法名称
        self.env_name = 'CliffWalking-v0'  # 环境名称
        self.device = torch.device(
            "cuda" if torch.cuda.is_available() else "cpu")  # 检测GPUgjgjlkhfsf风刀霜的撒发十
        self.seed = 10 # 随机种子，置0则不设置随机种子
        self.train_eps = 400  # 训练的回合数
        self.test_eps = 30  # 测试的回合数
        ################################################################################
        
        ################################## 算法超参数 ###################################
        self.gamma = 0.90  # 强化学习中的折扣因子
        self.epsilon_start = 0.95  # e-greedy策略中初始epsilon
        self.epsilon_end = 0.01  # e-greedy策略中的终止epsilon
        self.epsilon_decay = 300  # e-greedy策略中epsilon的衰减率
        self.lr = 0.1  # 学习率
        ################################################################################
        
        ################################# 保存结果相关参数 ################################
        self.result_path = curr_path + "/outputs/" + self.env_name + \
            '/' + curr_time + '/results/'  # 保存结果的路径
        self.model_path = curr_path + "/outputs/" + self.env_name + \
            '/' + curr_time + '/models/'  # 保存模型的路径
        self.save = True # 是否保存图片
        ################################################################################
        
def train(cfg,env,agent):
    print('开始训练！')
    print(f'环境:{cfg.env_name}, 算法:{cfg.algo_name}, 设备:{cfg.device}')
    rewards = []  # 记录奖励
    ma_rewards = [] # 记录滑动平均奖励
    for i_ep in range(cfg.train_eps):
        ep_reward = 0  # 记录每个回合的奖励
        state = env.reset()  # 重置环境,即开始新的回合
        while True:
            action = agent.choose_action(state)  # 根据算法选择一个动作
            next_state, reward, done, _ = env.step(action)  # 与环境进行一次动作交互
            agent.update(state, action, reward, next_state, done)  # Q学习算法更新
            state = next_state  # 更新状态
            ep_reward += reward
            if done:
                break
        rewards.append(ep_reward)
        if ma_rewards:
            ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
        else:
            ma_rewards.append(ep_reward)
        print("回合数：{}/{}，奖励{:.1f}".format(i_ep+1, cfg.train_eps,ep_reward))
    print('完成训练！')
    return rewards,ma_rewards
    
def test(cfg,env,agent):
    print('开始测试！')
    print(f'环境：{cfg.env_name}, 算法：{cfg.algo_name}, 设备：{cfg.device}')
    rewards = []  # 记录所有回合的奖励
    ma_rewards = [] # 滑动平均的奖励
    for i_ep in range(cfg.test_eps):
        ep_reward = 0  # 记录每个episode的reward
        state = env.reset()  # 重置环境, 重新开一局（即开始新的一个回合）
        while True:
            action = agent.predict(state)  # 根据算法选择一个动作
            next_state, reward, done, _ = env.step(action)  # 与环境进行一个交互
            state = next_state  # 更新状态
            ep_reward += reward
            if done:
                break
        rewards.append(ep_reward)
        if ma_rewards:
            ma_rewards.append(ma_rewards[-1]*0.9+ep_reward*0.1)
        else:
            ma_rewards.append(ep_reward)
        print(f"回合数：{i_ep+1}/{cfg.test_eps}, 奖励：{ep_reward:.1f}")
    print('完成测试！')
    return rewards,ma_rewards
        
def env_agent_config(cfg,seed=1):
    '''创建环境和智能体
    Args:
        cfg ([type]): [description]
        seed (int, optional): 随机种子. Defaults to 1.
    Returns:
        env [type]: 环境
        agent : 智能体
    '''    
    env = gym.make(cfg.env_name)  
    env = CliffWalkingWapper(env)
    env.seed(seed) # 设置随机种子
    n_states = env.observation_space.n # 状态维度
    n_actions = env.action_space.n # 动作维度
    agent = QLearning(n_states,n_actions,cfg)
    return env,agent
if __name__ == "__main__":
    cfg = Config()
    # 训练
    env, agent = env_agent_config(cfg, seed=1)
    rewards, ma_rewards = train(cfg, env, agent)
    make_dir(cfg.result_path, cfg.model_path)  # 创建保存结果和模型路径的文件夹
    agent.save(path=cfg.model_path)  # 保存模型
    save_results(rewards, ma_rewards, tag='train',
                path=cfg.result_path)  # 保存结果
    plot_rewards(rewards, ma_rewards, cfg, tag="train")  # 画出结果
    # 测试
    env, agent = env_agent_config(cfg, seed=10)
    agent.load(path=cfg.model_path)  # 导入模型
    rewards, ma_rewards = test(cfg, env, agent)
    save_results(rewards, ma_rewards, tag='test', path=cfg.result_path)  # 保存结果
    plot_rewards(rewards, ma_rewards, cfg, tag="test")  # 画出结果