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 Tensorflow 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)
tf.set_random_seed(training_seed)
self.rank = get_rank()
def __init__(
self,
seed: int,
behavior_spec: BehaviorSpec,
trainer_settings: TrainerSettings,
tanh_squash: bool = False,
reparameterize: bool = False,
condition_sigma_on_obs: bool = True,
create_tf_graph: bool = True,
):
"""
Initialized the policy.
:param seed: Random seed to use for TensorFlow.
:param brain: The corresponding Brain for this policy.
:param trainer_settings: The trainer parameters.
"""
super().__init__(
seed,
behavior_spec,
trainer_settings,
tanh_squash,
reparameterize,
condition_sigma_on_obs,
)
if (
self.behavior_spec.action_spec.continuous_size > 0
and self.behavior_spec.action_spec.discrete_size > 0
):
raise UnityPolicyException(
"TensorFlow does not support mixed action spaces. Please run with the Torch framework."
)
# for ghost trainer save/load snapshots
self.assign_phs: List[tf.Tensor] = []
self.assign_ops: List[tf.Operation] = []
self.update_dict: Dict[str, tf.Tensor] = {}
self.inference_dict: Dict[str, tf.Tensor] = {}
self.first_normalization_update: bool = False
self.graph = tf.Graph()
self.sess = tf.Session(
config=tf_utils.generate_session_config(), graph=self.graph
)
self._initialize_tensorflow_references()
self.grads = None
self.update_batch: Optional[tf.Operation] = None
self.trainable_variables: List[tf.Variable] = []
self.rank = get_rank()
if create_tf_graph:
self.create_tf_graph()
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()