def __init__(self,
predictor_io_names,
player,
batch_size=32,
memory_size=1e6,
init_memory_size=50000,
exploration=1,
end_exploration=0.1,
exploration_epoch_anneal=0.002,
reward_clip=None,
update_frequency=1,
history_len=1):
"""
:param predictor: a callabale running the up-to-date network.
called with a state, return a distribution.
:param player: an `RLEnvironment`
:param history_len: length of history frames to concat. zero-filled initial frames
:param update_frequency: number of new transitions to add to memory
after sampling a batch of transitions for training
"""
init_memory_size = int(init_memory_size)
for k, v in locals().items():
if k != 'self':
setattr(self, k, v)
self.num_actions = player.get_action_space().num_actions()
logger.info("Number of Legal actions: {}".format(self.num_actions))
self.mem = deque(maxlen=int(memory_size))
self.rng = get_rng(self)
self._init_memory_flag = threading.Event(
) # tell if memory has been initialized
self._predictor_io_names = predictor_io_names
def __init__(self, config, rng=None, load=True, shuffle=False):
self.config = config
self.rng = get_rng(rng)
self.inputs, self.outputs, self.codes, self.code_lengths = {}, {}, {}, {}
self.input_strings, self.output_strings = {}, {}
self.with_input_string = False
self.iterator = {}
self._inputs, self._outputs, self._codes, self._code_lengths = {}, {}, {}, {}
self._input_strings, self._output_strings = {}, {}
self.data_names = ['train', 'test', 'val']
self.data_paths = {
key: os.path.join(config.data_dir, '{}.{}'.format(key, config.data_ext)) \
for key in self.data_names
}
if load:
self.load_data()
for name in self.data_names:
self.build_tf_data(name)
if shuffle:
self.shuffle()
self.generator = Generator(config.world_width,
colors=config.colors,
shapes=config.shapes)
def __init__(self, rng=None, min_int=0, max_int=19, debug=False, **kwargs):
super(KarelParser, self).__init__(**kwargs)
self.debug = debug
self.min_int = min_int
self.max_int = max_int
self.rng = get_rng(rng)
def reset_state(self):
self.rng = get_rng()
def __init__(self, datalist, is_train=True, shuffle=True):
self.rng = get_rng()
self._datalist = datalist
self._shuffle = shuffle
self._is_train = is_train