def __init__(
self,
trainer_factory: TrainerFactory,
output_path: str,
run_id: str,
param_manager: EnvironmentParameterManager,
train: bool,
training_seed: int,
):
"""
:param output_path: Path to save the model.
:param summaries_dir: Folder to save training summaries.
:param run_id: The sub-directory name for model and summary statistics
:param param_manager: EnvironmentParameterManager object which stores information about all
environment parameters.
:param train: Whether to train model, or only run inference.
:param training_seed: Seed to use for Numpy and Torch random number generation.
:param threaded: Whether or not to run trainers in a separate thread. Disable for testing/debugging.
"""
self.trainers: Dict[str, Trainer] = {}
self.brain_name_to_identifier: Dict[str, Set] = defaultdict(set)
self.trainer_factory = trainer_factory
self.output_path = output_path
self.logger = get_logger(__name__)
self.run_id = run_id
self.train_model = train
self.param_manager = param_manager
self.ghost_controller = self.trainer_factory.ghost_controller
self.registered_behavior_ids: Set[str] = set()
self.trainer_threads: List[threading.Thread] = []
self.kill_trainers = False
np.random.seed(training_seed)
torch_utils.torch.manual_seed(training_seed)
self.rank = get_rank()
def __init__(self):
self.training_start_time = time.time()
# If self-play, we want to print ELO as well as reward
self.self_play = False
self.self_play_team = -1
self.rank = get_rank()