a3c/train.py

import os
import torch
import torch.multiprocessing as mp
from .shared_adam import SharedAdam
from .net import Net
from .worker import Worker


def train(env, max_ep, model_checkpoint_dir, gamma=0., pretrained_model_path=None, save=False, min_reward=9.9, every_n_save=100):
    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
    if pretrained_model_path:
        gnet.load_state_dict(torch.load(pretrained_model_path))
    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, gamma, pretrained_model_path, save, min_reward, every_n_save) 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]
    if save:
        torch.save(gnet.state_dict(), os.path.join(model_checkpoint_dir, f'model_{env.unwrapped.spec.id}.pth'))
    return global_ep, win_ep, gnet, res