#!/usr/bin/env python
# coding=utf-8
'''
Author: JiangJi
Email: johnjim0816@gmail.com
Date: 2021-11-07 18:10:37
LastEditor: JiangJi
LastEditTime: 2021-11-19 18:34:05
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
    
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！')
    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！')
    return rewards,ma_rewards