import functools
import os
import queue
import numpy as np
import libhpref
from tartist import random, image
from tartist.app import rl
from tartist.core import get_env, get_logger
from tartist.core.utils.cache import cached_result
from tartist.core.utils.naming import get_dump_directory
from tartist.data import flow
from tartist.nn import opr as O, optimizer, summary
logger = get_logger(__file__)
__envs__ = {
'dir': {
'root': get_dump_directory(__file__),
},
'a3c': {
'env_name': 'Breakout-v0',
'input_shape': (84, 84),
'nr_history_frames': 4,
'max_nr_steps': 40000,
'gamma': 0.99,
'nr_td_steps': 5,
'nr_players': 2,
'nr_predictors': 2,
'predictor': {
# -*- coding:utf8 -*- # File : snapshot.py # Author : Jiayuan Mao # Email : [email protected] # Date : 2/26/17 # # This file is part of TensorArtist. from tartist.core import get_env, register_event, get_logger from tartist.core import io import os.path as osp import numpy as np logger = get_logger() __snapshot_dir__ = 'snapshots' __snapshot_ext__ = '.snapshot.pkl' __weights_ext__ = '.weights.pkl' def get_snapshot_dir(): return get_env('dir.snapshot', osp.join(get_env('dir.root'), __snapshot_dir__)) def enable_snapshot_saver(trainer, save_interval=1): def dump_snapshot_on_epoch_after(trainer): if trainer.epoch % save_interval != 0: return snapshot_dir = get_snapshot_dir() snapshot = trainer.dump_snapshot()
