#!/usr/bin/env python
# coding=utf-8
'''
Author: John
Email: johnjim0816@gmail.com
Date: 2020-11-22 23:21:53
LastEditor: John
LastEditTime: 2022-08-22 17:40:07
Discription: 
Environment: 
'''
import sys,os
curr_path = os.path.dirname(os.path.abspath(__file__))  # current path
parent_path = os.path.dirname(curr_path)  # parent path
sys.path.append(parent_path)  # add to system path

import gym
import torch
import datetime
import argparse
from itertools import count
import torch.nn.functional as F
from pg import PolicyGradient
from common.utils import save_results, make_dir,all_seed,save_args,plot_rewards
from common.models import MLP
from common.memories import PGReplay


def get_args():
    """ Hyperparameters
    """
    curr_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")  # Obtain current time
    parser = argparse.ArgumentParser(description="hyperparameters")      
    parser.add_argument('--algo_name',default='PolicyGradient',type=str,help="name of algorithm")
    parser.add_argument('--env_name',default='CartPole-v0',type=str,help="name of environment")
    parser.add_argument('--train_eps',default=200,type=int,help="episodes of training")
    parser.add_argument('--test_eps',default=20,type=int,help="episodes of testing")
    parser.add_argument('--gamma',default=0.99,type=float,help="discounted factor")
    parser.add_argument('--lr',default=0.005,type=float,help="learning rate")
    parser.add_argument('--update_fre',default=8,type=int)
    parser.add_argument('--hidden_dim',default=36,type=int)
    parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda") 
    parser.add_argument('--seed',default=1,type=int,help="seed") 
    parser.add_argument('--result_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
            '/' + curr_time + '/results/' )
    parser.add_argument('--model_path',default=curr_path + "/outputs/" + parser.parse_args().env_name + \
            '/' + curr_time + '/models/' ) # path to save models
    parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")   
    parser.add_argument('--show_fig',default=False,type=bool,help="if show figure or not")           
    args = parser.parse_args([])                          
    return args

class PGNet(MLP):
    ''' instead of outputing action, PG Net outputs propabilities of actions, we can use class inheritance from MLP here
    '''
    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = F.sigmoid(self.fc3(x))
        return x

def env_agent_config(cfg):
    env = gym.make(cfg.env_name)  
    if cfg.seed !=0: # set random seed
        all_seed(env,seed=cfg.seed)
    n_states = env.observation_space.shape[0]
    n_actions = env.action_space.n  # action dimension
    print(f"state dim: {n_states}, action dim: {n_actions}")
    model = PGNet(n_states,1,hidden_dim=cfg.hidden_dim)
    memory = PGReplay()
    agent = PolicyGradient(n_states,model,memory,cfg)
    return env,agent

def train(cfg,env,agent):
    print('Start training!')
    print(f'Env:{cfg.env_name}, Algo:{cfg.algo_name}, Device:{cfg.device}')
    rewards = []
    for i_ep in range(cfg.train_eps):
        state = env.reset()
        ep_reward = 0
        for _ in count():
            action = agent.sample_action(state) # sample action
            next_state, reward, done, _ = env.step(action)
            ep_reward += reward
            if done:
                reward = 0
            agent.memory.push((state,float(action),reward))
            state = next_state
            if done:
                print(f'Episode：{i_ep+1}/{cfg.train_eps}, Reward:{ep_reward:.2f}')
                break
        if (i_ep+1) % cfg.update_fre == 0:
            agent.update()
        rewards.append(ep_reward)
    print('Finish training!')
    env.close() # close environment
    res_dic = {'episodes':range(len(rewards)),'rewards':rewards}
    return res_dic
            

def test(cfg,env,agent):
    print("start testing!")
    print(f"Env: {cfg.env_name}, Algo: {cfg.algo_name}, Device: {cfg.device}")
    rewards = []
    for i_ep in range(cfg.test_eps):
        state = env.reset()
        ep_reward = 0
        for _ in count():
            action = agent.predict_action(state)
            next_state, reward, done, _ = env.step(action)
            ep_reward += reward
            if done:
                reward = 0
            state = next_state
            if done:
                print(f'Episode: {i_ep+1}/{cfg.test_eps}，Reward: {ep_reward:.2f}')
                break
        rewards.append(ep_reward)
    print("finish testing!")
    env.close()
    return {'episodes':range(len(rewards)),'rewards':rewards}
    
if __name__ == "__main__":
    cfg = get_args()
    env, agent = env_agent_config(cfg)
    res_dic = train(cfg, env, agent)
    save_args(cfg,path = cfg.result_path) # save parameters
    agent.save_model(path = cfg.model_path)  # save models
    save_results(res_dic, tag = 'train', path = cfg.result_path) # save results
    plot_rewards(res_dic['rewards'], cfg, path = cfg.result_path,tag = "train")  # plot results
    # testing
    env, agent = env_agent_config(cfg) # create new env for testing, sometimes can ignore this step
    agent.load_model(path = cfg.model_path)  # load model
    res_dic = test(cfg, env, agent)
    save_results(res_dic, tag='test',
                 path = cfg.result_path)  
    plot_rewards(res_dic['rewards'], cfg, path = cfg.result_path,tag = "test")