import sys
import 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, plot_rewards_cn
from DQN.agent import DQN
from DQN.train import train,test


curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # 获取当前时间
algo_name = "DQN"  # 算法名称
env_name = 'CartPole-v1'  # 环境名称
class DQNConfig:
    ''' 算法相关参数设置
    '''

    def __init__(self):
        self.algo_name = algo_name  # 算法名称
        self.env_name = env_name  # 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.eval_eps = 30  # 测试的回合数
        # 超参数
        self.gamma = 0.95  # 强化学习中的折扣因子
        self.epsilon_start = 0.90  # 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 = 4  # 目标网络的更新频率
        self.hidden_dim = 256  # 网络隐藏层
class PlotConfig:
    ''' 绘图相关参数设置
    '''

    def __init__(self) -> None:
        self.algo_name = algo_name  # 算法名称
        self.env_name = env_name  # 环境名称
        self.device = torch.device(
            "cuda" if torch.cuda.is_available() else "cpu")  # 检测GPU
        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):
    ''' 创建环境和智能体
    '''
    env = gym.make(cfg.env_name)  # 创建环境
    env.seed(seed)  # 设置随机种子
    state_dim = env.observation_space.shape[0]  # 状态数
    action_dim = env.action_space.n  # 动作数
    agent = DQN(state_dim, action_dim, cfg)  # 创建智能体
    return env, agent


cfg = DQNConfig()
plot_cfg = PlotConfig()
# 训练
env, agent = env_agent_config(cfg, seed=1)
rewards, ma_rewards = train(cfg, env, agent)
make_dir(plot_cfg.result_path, plot_cfg.model_path)  # 创建保存结果和模型路径的文件夹
agent.save(path=plot_cfg.model_path)  # 保存模型
save_results(rewards, ma_rewards, tag='train',
             path=plot_cfg.result_path)  # 保存结果
plot_rewards_cn(rewards, ma_rewards, plot_cfg, tag="train")  # 画出结果
# 测试
env, agent = env_agent_config(cfg, seed=10)
agent.load(path=plot_cfg.model_path)  # 导入模型
rewards, ma_rewards = test(cfg, env, agent)
save_results(rewards, ma_rewards, tag='test',
             path=plot_cfg.result_path)  # 保存结果
plot_rewards_cn(rewards, ma_rewards, plot_cfg, tag="test")  # 画出结果