def create_bc_module(mock_behavior_specs, bc_settings, use_rnn, tanhresample):
# model_path = env.external_brain_names[0]
trainer_config = TrainerSettings()
trainer_config.network_settings.memory = (NetworkSettings.MemorySettings()
if use_rnn else None)
policy = NNPolicy(
0,
mock_behavior_specs,
trainer_config,
False,
"test",
False,
tanhresample,
tanhresample,
)
with policy.graph.as_default():
bc_module = BCModule(
policy,
policy_learning_rate=trainer_config.hyperparameters.learning_rate,
default_batch_size=trainer_config.hyperparameters.batch_size,
default_num_epoch=3,
settings=bc_settings,
)
policy.initialize_or_load(
) # Normally the optimizer calls this after the BCModule is created
return bc_module
def create_bc_module(mock_brain, trainer_config, use_rnn, demo_file,
tanhresample):
# model_path = env.external_brain_names[0]
trainer_config["model_path"] = "testpath"
trainer_config["keep_checkpoints"] = 3
trainer_config["use_recurrent"] = use_rnn
trainer_config["behavioral_cloning"]["demo_path"] = (
os.path.dirname(os.path.abspath(__file__)) + "/" + demo_file)
policy = NNPolicy(0, mock_brain, trainer_config, False, False,
tanhresample, tanhresample)
with policy.graph.as_default():
bc_module = BCModule(
policy,
policy_learning_rate=trainer_config["learning_rate"],
default_batch_size=trainer_config["batch_size"],
default_num_epoch=3,
**trainer_config["behavioral_cloning"],
)
policy.initialize_or_load(
) # Normally the optimizer calls this after the BCModule is created
return bc_module
