#!/usr/bin/env python
# coding=utf-8
'''
@Author: John
@Email: johnjim0816@gmail.com
@Date: 2020-06-11 20:58:21
@LastEditor: John
LastEditTime: 2022-02-10 06:23:27
@Discription: 
@Environment: python 3.7.7
'''
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sys.path

import datetime
import gym
import torch

from env import NormalizedActions,OUNoise
from ddpg import DDPG
from DDPG.train import train,test
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 = 'DDPG'  # 算法名称
        self.env_name = 'Pendulum-v1'  # 环境名称，gym新版本（约0.21.0之后）中Pendulum-v0改为Pendulum-v1
        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 = 50 # 测试的回合数
        ################################################################################
        
        ################################## 算法超参数 ###################################
        self.gamma = 0.99 # 折扣因子
        self.critic_lr = 1e-3 # 评论家网络的学习率
        self.actor_lr = 1e-4 # 演员网络的学习率
        self.memory_capacity = 8000 # 经验回放的容量
        self.batch_size = 128 # 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 # 网络隐藏层维度
        self.soft_tau = 1e-2 # 软更新参数
        ################################################################################
        
        ################################# 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 = NormalizedActions(gym.make(cfg.env_name)) # 装饰action噪声
    env.seed(seed) # 随机种子
    n_states = env.observation_space.shape[0]
    n_actions = env.action_space.shape[0]
    agent = DDPG(n_states,n_actions,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}，设备：{cfg.device}')
    ou_noise = OUNoise(env.action_space)  # 动作噪声
    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):
        state = env.reset()
        ou_noise.reset()
        done = False
        ep_reward = 0
        i_step = 0
        while not done:
            i_step += 1
            action = agent.choose_action(state)
            action = ou_noise.get_action(action, i_step) 
            next_state, reward, done, _ = env.step(action)
            ep_reward += reward
            agent.memory.push(state, action, reward, next_state, done)
            agent.update()
            state = next_state
        if (i_ep+1)%10 == 0:
            print('回合：{}/{}，奖励：{:.2f}'.format(i_ep+1, cfg.train_eps, 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}, 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.test_eps):
        state = env.reset() 
        done = False
        ep_reward = 0
        i_step = 0
        while not done:
            i_step += 1
            action = agent.choose_action(state)
            next_state, reward, done, _ = env.step(action)
            ep_reward += reward
            state = next_state
        print('回合：{}/{}, 奖励：{}'.format(i_ep+1, cfg.train_eps, 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(f"回合：{i_ep+1}/{cfg.test_eps}，奖励：{ep_reward:.1f}")
    print('完成测试！')
    return rewards, ma_rewards
if __name__ == "__main__":
    cfg = Config()
    # 训练
    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, cfg, tag="train")  # 画出结果
    # 测试
    env,agent = env_agent_config(cfg,seed=10)
    agent.load(path=cfg.model_path)
    rewards,ma_rewards = test(cfg,env,agent)
    save_results(rewards,ma_rewards,tag = 'test',path = cfg.result_path)
    plot_rewards(rewards, ma_rewards, cfg, tag="test")  # 画出结果