#!/usr/bin/env python
# coding=utf-8
'''
Author: John
Email: johnjim0816@gmail.com
Date: 2021-03-11 17:59:16
LastEditor: John
LastEditTime: 2022-08-25 14:26:36
Discription: 
Environment: 
'''
import sys,os
os.environ["KMP_DUPLICATE_LIB_OK"]  =  "TRUE" # avoid "OMP: Error #15: Initializing libiomp5md.dll, but found libiomp5md.dll already initialized."
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 path to system path
import gym
import datetime
import argparse
from envs.register import register_env
from envs.wrappers import CliffWalkingWapper
from Sarsa.sarsa import Sarsa
from common.utils import save_results,make_dir,plot_rewards,save_args,all_seed

def get_args():
    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='Sarsa',type=str,help="name of algorithm")
    parser.add_argument('--env_name',default='Racetrack-v0',type=str,help="name of environment")
    parser.add_argument('--train_eps',default=300,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('--epsilon_start',default=0.90,type=float,help="initial value of epsilon") 
    parser.add_argument('--epsilon_end',default=0.01,type=float,help="final value of epsilon") 
    parser.add_argument('--epsilon_decay',default=200,type=int,help="decay rate of epsilon") 
    parser.add_argument('--lr',default=0.2,type=float,help="learning rate")
    parser.add_argument('--device',default='cpu',type=str,help="cpu or cuda") 
    parser.add_argument('--seed',default=10,type=int,help="seed") 
    parser.add_argument('--show_fig',default=False,type=bool,help="if show figure or not")  
    parser.add_argument('--save_fig',default=True,type=bool,help="if save figure or not")   
    args = parser.parse_args()   
    default_args = {'result_path':f"{curr_path}/outputs/{args.env_name}/{curr_time}/results/",
                    'model_path':f"{curr_path}/outputs/{args.env_name}/{curr_time}/models/",
    }
    args = {**vars(args),**default_args}  # type(dict)                         
    return args

def env_agent_config(cfg):
    register_env(cfg['env_name'])
    env = gym.make(cfg['env_name'])
    if cfg['seed'] !=0: # set random seed
        all_seed(env,seed= cfg['seed']) 
    if cfg['env_name'] == 'CliffWalking-v0':
        env = CliffWalkingWapper(env)
    try: # state dimension
        n_states = env.observation_space.n # print(hasattr(env.observation_space, 'n'))
    except AttributeError:
        n_states = env.observation_space.shape[0] # print(hasattr(env.observation_space, 'shape'))
    n_actions = env.action_space.n  # action dimension
    print(f"n_states: {n_states}, n_actions: {n_actions}")
    cfg.update({"n_states":n_states,"n_actions":n_actions}) # update to cfg paramters
    agent = Sarsa(cfg)
    return env,agent
        
def train(cfg,env,agent):
    print("Start training!")
    print(f"Env: {cfg['env_name']}, Algorithm: {cfg['algo_name']}, Device: {cfg['device']}")
    rewards = []  # record rewards for all episodes
    steps = [] # record steps for all episodes
    for i_ep in range(cfg['train_eps']):
        ep_reward = 0  # reward per episode
        ep_step = 0 # step per episode
        state = env.reset()  # reset and obtain initial state
        action = agent.sample_action(state)
        while True:
        # for _ in range(cfg.ep_max_steps):
            next_state, reward, done, _ = env.step(action)  # update env and return transitions
            next_action =  agent.sample_action(next_state)
            agent.update(state, action, reward, next_state, next_action,done)  # update agent
            state = next_state  # update state
            action = next_action
            ep_reward += reward
            ep_step += 1
            if done:
                break
        rewards.append(ep_reward)
        steps.append(ep_step)
        if (i_ep+1)%10==0:
            print(f'Episode: {i_ep+1}/{cfg["train_eps"]}, Reward: {ep_reward:.2f}, Steps:{ep_step}, Epislon: {agent.epsilon:.3f}')
    print("Finish training!")
    return {'episodes':range(len(rewards)),'rewards':rewards,'steps':steps}

def test(cfg,env,agent):
    print("Start testing!")
    print(f"Env: {cfg['env_name']}, Algorithm: {cfg['algo_name']}, Device: {cfg['device']}")
    rewards = []  # record rewards for all episodes
    steps = [] # record steps for all episodes
    for i_ep in range(cfg['test_eps']):
        ep_reward = 0  # reward per episode
        ep_step = 0
        while True:
        # for _ in range(cfg.ep_max_steps):
            action = agent.predict_action(state)
            next_state, reward, done = env.step(action)
            state = next_state
            ep_reward+=reward
            ep_step+=1
            if done:
                break  
        rewards.append(ep_reward)
        steps.append(ep_step)
        print(f"Episode: {i_ep+1}/{cfg['test_eps']}, Steps:{ep_step}, Reward: {ep_reward:.2f}")
    print("Finish testing!")
    return {'episodes':range(len(rewards)),'rewards':rewards,'steps':steps}

if __name__ == "__main__":
    cfg = get_args()
    # 训练
    env, agent = env_agent_config(cfg)
    res_dic = train(cfg, env, agent)
    make_dir(cfg.result_path, cfg.model_path)  
    save_args(cfg) # save parameters
    agent.save(path=cfg.model_path)  # save model
    save_results(res_dic, tag='train',
                 path=cfg.result_path)  
    plot_rewards(res_dic['rewards'], cfg, tag="train")  
    # 测试
    env, agent = env_agent_config(cfg)
    agent.load(path=cfg.model_path)  # 导入模型
    res_dic = test(cfg, env, agent)
    save_results(res_dic, tag='test',
                 path=cfg.result_path)  # 保存结果
    plot_rewards(res_dic['rewards'], cfg, tag="test")  # 画出结果