Spaces:
Sleeping
Sleeping
| import os | |
| import numpy as np | |
| import random | |
| import torch | |
| import torch.multiprocessing as mp | |
| from .shared_adam import SharedAdam | |
| from .net import Net | |
| from .worker import Worker | |
| def _set_seed(seed: int = 100) -> None: | |
| np.random.seed(seed) | |
| random.seed(seed) | |
| torch.manual_seed(seed) | |
| if torch.cuda.is_available(): | |
| torch.cuda.manual_seed(seed) | |
| # When running on the CuDNN backend, two further options must be set | |
| torch.backends.cudnn.deterministic = True | |
| torch.backends.cudnn.benchmark = False | |
| # Set a fixed value for the hash seed | |
| os.environ["PYTHONHASHSEED"] = str(seed) | |
| def train(env, max_ep, model_checkpoint_dir, gamma=0., seed=100, 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]) | |
| # Set global seeds for randoms | |
| _set_seed(seed) | |
| 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 | |