"""
Reinforcement Learning (A3C) using Pytroch + multiprocessing.
The most simple implementation for continuous action.

View more on my Chinese tutorial page [莫烦Python](https://morvanzhou.github.io/).
"""
import os
import torch.multiprocessing as mp
from .utils import v_wrap, push_and_pull, record, save_model
from .shared_adam import SharedAdam
from .net import Net

GAMMA = 0.65

class Worker(mp.Process):
    def __init__(self, max_ep, gnet, opt, global_ep, global_ep_r, res_queue, name, env, N_S, N_A, words_list, word_width, winning_ep, model_checkpoint_dir):
        super(Worker, self).__init__()
        self.max_ep = max_ep
        self.name = 'w%02i' % name
        self.g_ep, self.g_ep_r, self.res_queue, self.winning_ep = global_ep, global_ep_r, res_queue, winning_ep
        self.gnet, self.opt = gnet, opt
        self.word_list = words_list
        self.lnet = Net(N_S, N_A, words_list, word_width)           # local network
        self.env = env.unwrapped
        self.model_checkpoint_dir = model_checkpoint_dir

    def run(self):
        while self.g_ep.value < self.max_ep:
            s = self.env.reset()
            buffer_s, buffer_a, buffer_r = [], [], []
            ep_r = 0.
            while True:
                a = self.lnet.choose_action(v_wrap(s[None, :]))
                s_, r, done, _ = self.env.step(a)
                ep_r += r
                buffer_a.append(a)
                buffer_s.append(s)
                buffer_r.append(r)

                if done:  # update global and assign to local net
                    # sync
                    push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA)
                    goal_word = self.word_list[self.env.goal_word]
                    record( self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name, goal_word, self.word_list[a], len(buffer_a), self.winning_ep)
                    save_model(self.gnet, self.model_checkpoint_dir, self.g_ep.value, self.g_ep_r.value)
                    buffer_s, buffer_a, buffer_r = [], [], []
                    break
                s = s_
        self.res_queue.put(None)


def train(env, max_ep, model_checkpoint_dir):
    os.environ["OMP_NUM_THREADS"] = "1"
    if not os.path.exists(model_checkpoint_dir):
        os.makedirs(model_checkpoint_dir)
    n_s = env.observation_space.shape[0]
    n_a = env.action_space.n
    words_list = env.words
    word_width = len(env.words[0])
    gnet = Net(n_s, n_a, words_list, word_width)        # global network
    gnet.share_memory()         # share the global parameters in multiprocessing
    opt = SharedAdam(gnet.parameters(), lr=1e-4, betas=(0.92, 0.999))      # global optimizer
    global_ep, global_ep_r, res_queue, win_ep = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Value('i', 0)

    # parallel training
    workers = [Worker(max_ep, gnet, opt, global_ep, global_ep_r, res_queue, i, env, n_s, n_a, words_list, word_width, win_ep, model_checkpoint_dir) for i in range(mp.cpu_count())]
    [w.start() for w in workers]
    res = []                    # record episode reward to plot
    while True:
        r = res_queue.get()
        if r is not None:
            res.append(r)
        else:
            break
    [w.join() for w in workers]
    return global_ep, win_ep, gnet, res