#!/usr/bin/env python
# coding=utf-8
'''
@Author: John
@Email: johnjim0816@gmail.com
@Date: 2020-06-12 00:48:57
@LastEditor: John
LastEditTime: 2021-04-18 14:44:45
@Discription: 
@Environment: python 3.7.7
'''
from common.utils import save_results, make_dir, del_empty_dir
from common.plot import plot_rewards
from DQN.agent import DQN
import datetime
import torch
import gym
import sys
import os
curr_path = os.path.dirname(__file__)
parent_path = os.path.dirname(curr_path)
sys.path.append(parent_path)  # add current terminal path to sys.path


curr_time = datetime.datetime.now().strftime(
    "%Y%m%d-%H%M%S")  # obtain current time


class DQNConfig:
    def __init__(self):
        self.algo = "DQN"  # name of algo
        self.env = 'CartPole-v0'
        self.result_path = curr_path+"/outputs/" + self.env + \
            '/'+curr_time+'/results/'  # path to save results
        self.model_path = curr_path+"/outputs/" + self.env + \
            '/'+curr_time+'/models/'  # path to save results
        self.gamma = 0.95
        self.epsilon_start = 1  # e-greedy策略的初始epsilon
        self.epsilon_end = 0.01
        self.epsilon_decay = 500
        self.lr = 0.0001  # learning rate
        self.memory_capacity = 10000  # Replay Memory容量
        self.batch_size = 32
        self.train_eps = 300  # 训练的episode数目
        self.target_update = 2  # target net的更新频率
        self.eval_eps = 20  # 测试的episode数目
        self.device = torch.device(
            "cuda" if torch.cuda.is_available() else "cpu")  # 检测gpu
        self.hidden_dim = 256  # 神经网络隐藏层维度


def train(cfg, env, agent):
    print('Start to train !')
    print(f'Env:{cfg.env}, Algorithm:{cfg.algo}, Device:{cfg.device}')
    rewards = []
    ma_rewards = []  # moveing average reward
    for i_episode in range(cfg.train_eps):
        state = env.reset()
        done = False
        ep_reward = 0
        while not done:
            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 i_episode % cfg.target_update == 0:
            agent.target_net.load_state_dict(agent.policy_net.state_dict())
        print('Episode:{}/{}, Reward:{}'.format(i_episode+1, cfg.train_eps, ep_reward))
        rewards.append(ep_reward)
        # 计算滑动窗口的reward
        if ma_rewards:
            ma_rewards.append(0.9*ma_rewards[-1]+0.1*ep_reward)
        else:
            ma_rewards.append(ep_reward)
    print('Complete training！')
    return rewards, ma_rewards


if __name__ == "__main__":
    cfg = DQNConfig()
    env = gym.make(cfg.env)
    env.seed(1)
    state_dim = env.observation_space.shape[0]
    action_dim = env.action_space.n
    agent = DQN(state_dim, action_dim, cfg)
    rewards, ma_rewards = train(cfg, env, agent)
    make_dir(cfg.result_path, cfg.model_path)
    agent.save(path=cfg.model_path)
    save_results(rewards, ma_rewards, tag='train', path=cfg.result_path)
    plot_rewards(rewards, ma_rewards, tag="train",
                 algo=cfg.algo, path=cfg.result_path)