#!/usr/bin/env python
# coding=utf-8
'''
Author: John
Email: johnjim0816@gmail.com
Date: 2020-11-22 23:21:53
LastEditor: John
LastEditTime: 2022-02-10 06:13:21
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 itertools import count

from pg import PolicyGradient
from common.utils import save_results, make_dir
from common.utils import plot_rewards

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 = "PolicyGradient"  # 算法名称
        self.env_name = 'CartPole-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 = 300 # 训练的回合数
        self.test_eps = 30 # 测试的回合数
        ################################################################################
        
        ################################## 算法超参数 ###################################
        self.batch_size = 8 # mini-batch SGD中的批量大小
        self.lr = 0.01 # 学习率
        self.gamma = 0.99 # 强化学习中的折扣因子
        self.hidden_dim = 36 # 网络隐藏层
        ################################################################################
        
        ################################# 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 env_agent_config(cfg,seed=1):
    env = gym.make(cfg.env_name)  
    env.seed(seed)
    n_states = env.observation_space.shape[0]
    agent = PolicyGradient(n_states,cfg)
    return env,agent

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}')
    state_pool = [] # 存放每batch_size个episode的state序列
    action_pool = []
    reward_pool = [] 
    rewards = []
    ma_rewards = []
    for i_ep in range(cfg.train_eps):
        state = env.reset()
        ep_reward = 0
        for _ in count():
            action = agent.choose_action(state) # 根据当前环境state选择action
            next_state, reward, done, _ = env.step(action)
            ep_reward += reward
            if done:
                reward = 0
            state_pool.append(state)
            action_pool.append(float(action))
            reward_pool.append(reward)
            state = next_state
            if done:
                print('回合：{}/{}, 奖励：{}'.format(i_ep + 1, cfg.train_eps, ep_reward))
                break
        if i_ep > 0 and i_ep % cfg.batch_size == 0:
            agent.update(reward_pool,state_pool,action_pool)
            state_pool = [] # 每个episode的state
            action_pool = []
            reward_pool = []
        rewards.append(ep_reward)
        if ma_rewards:
            ma_rewards.append(
                0.9*ma_rewards[-1]+0.1*ep_reward)
        else:
            ma_rewards.append(ep_reward)
    print('完成训练！')
    env.close()
    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 = []
    ma_rewards = []
    for i_ep in range(cfg.test_eps):
        state = env.reset()
        ep_reward = 0
        for _ in count():
            action = agent.choose_action(state) # 根据当前环境state选择action
            next_state, reward, done, _ = env.step(action)
            ep_reward += reward
            if done:
                reward = 0
            state = next_state
            if done:
                print('回合：{}/{}, 奖励：{}'.format(i_ep + 1, cfg.train_eps, ep_reward))
                break
        rewards.append(ep_reward)
        if ma_rewards:
            ma_rewards.append(
                0.9*ma_rewards[-1]+0.1*ep_reward)
        else:
            ma_rewards.append(ep_reward)
    print('完成测试！')
    env.close()
    return rewards, ma_rewards
    
if __name__ == "__main__":
    cfg = Config()
    # 训练
    env, agent = env_agent_config(cfg)
    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)
    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