#!/usr/bin/env python
# coding=utf-8
'''
@Author: John
@Email: johnjim0816@gmail.com
@Date: 2020-06-12 00:48:57
@LastEditor: John
LastEditTime: 2021-05-04 15:01:34
@Discription: 
@Environment: python 3.7.7
'''
import sys,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

import datetime
import torch
import gym

from common.utils import save_results, make_dir
from common.plot import plot_rewards
from DQN.agent import DQN

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.train_eps = 300  # 训练的episode数目
        self.eval_eps = 50 # number of episodes for evaluating
        self.gamma = 0.95
        self.epsilon_start = 0.90  # 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 = 100000  # Replay Memory容量
        self.batch_size = 64
        self.target_update = 2  # target net的更新频率
        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 env_agent_config(cfg,seed=1):
    env = gym.make(cfg.env)  
    env.seed(seed)
    state_dim = env.observation_space.shape[0]
    action_dim = env.action_space.n
    agent = DQN(state_dim,action_dim,cfg)
    return env,agent
    
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 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_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)
        # save ma rewards
        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

def eval(cfg,env,agent):
    rewards = []  # 记录所有episode的reward
    ma_rewards = [] # 滑动平均的reward
    for i_ep in range(cfg.eval_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episode）
        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  # 存储上一个观察值
            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"Episode:{i_ep+1}/{cfg.eval_eps}, reward:{ep_reward:.1f}")
    return rewards,ma_rewards

if __name__ == "__main__":
    cfg = DQNConfig()
    env,agent = env_agent_config(cfg,seed=1)
    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)

    env,agent = env_agent_config(cfg,seed=10)
    agent.load(path=cfg.model_path)
    rewards,ma_rewards = eval(cfg,env,agent)
    save_results(rewards,ma_rewards,tag='eval',path=cfg.result_path)
    plot_rewards(rewards,ma_rewards,tag="eval",env=cfg.env,algo = cfg.algo,path=cfg.result_path)