def start_trainer(self, trainer: Trainer, env_manager: EnvManager) -> None:
self.trainers[trainer.brain_name] = trainer
self.logger.info(trainer)
if self.train_model:
trainer.write_tensorboard_text("Hyperparameters",
trainer.parameters)
env_manager.set_policy(trainer.brain_name, trainer.policy)
def trainer_update_func(self, trainer: Trainer) -> None:
while not self.kill_trainers:
with hierarchical_timer("trainer_advance"):
trainer.advance()
