#!/usr/bin/env python
# coding=utf-8
'''
Author: JiangJi
Email: johnjim0816@gmail.com
Date: 2021-11-07 18:10:37
LastEditor: JiangJi
LastEditTime: 2021-12-29 15:02:30
Discription: 
'''

import sys,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 common.utils import save_results, make_dir
from common.utils import plot_rewards
from DoubleDQN.double_dqn import DoubleDQN

curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # 获取当前时间

class Config:
    def __init__(self):
        ################################## 环境超参数 ###################################
        self.algo_name = 'DoubleDQN' # 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GPU
        self.train_eps = 200  # 训练的回合数
        self.test_eps = 30  # 测试的回合数
        ################################################################################

        ################################## 算法超参数 ###################################
        self.gamma = 0.95  # 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 = 500  # e-greedy策略中epsilon的衰减率
        self.lr = 0.0001  # 学习率
        self.memory_capacity = 100000  # 经验回放的容量
        self.batch_size = 64  # mini-batch SGD中的批量大小
        self.target_update = 2  # 目标网络的更新频率
        self.hidden_dim = 256  # 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)
    state_dim = env.observation_space.shape[0]
    action_dim = env.action_space.n
    agent = DoubleDQN(state_dim,action_dim,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}')
    rewards = [] # 记录所有回合的奖励
    ma_rewards = []  # 记录所有回合的滑动平均奖励
    for i_ep in range(cfg.train_eps):
        ep_reward = 0 # 记录一回合内的奖励
        state = env.reset() # 重置环境，返回初始状态
        while True:
            action = agent.choose_action(state) 
            next_state, reward, done, _ = env.step(action)
            ep_reward += reward
            agent.memory.push(state, action, reward, next_state, done) 
            state = next_state 
            agent.update() 
            if done:
                break
        if i_ep % cfg.target_update == 0:
            agent.target_net.load_state_dict(agent.policy_net.state_dict())
        if (i_ep+1)%10 == 0: 
            print(f'回合：{i_ep+1}/{cfg.train_eps}，奖励：{ep_reward}')
        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}')
    ############# 由于测试不需要使用epsilon-greedy策略，所以相应的值设置为0 ###############
    cfg.epsilon_start = 0.0  # e-greedy策略中初始epsilon
    cfg.epsilon_end = 0.0  # e-greedy策略中的终止epsilon
    ################################################################################
    rewards = [] # 记录所有回合的奖励
    ma_rewards = []  # 记录所有回合的滑动平均奖励
    
    for i_ep in range(cfg.test_eps):
        state = env.reset() 
        ep_reward = 0   
        while True:
            action = agent.choose_action(state) 
            next_state, reward, done, _ = env.step(action)  
            state = next_state  
            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：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.1f}")
    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