def _create_trainer_and_manager(self, env_manager: EnvManager,
name_behavior_id: str) -> None:
brain_name = BehaviorIdentifiers.from_name_behavior_id(
name_behavior_id).brain_name
try:
trainer = self.trainers[brain_name]
except KeyError:
trainer = self.trainer_factory.generate(brain_name)
self.trainers[brain_name] = trainer
self.logger.info(trainer)
if self.train_model:
trainer.write_tensorboard_text("Hyperparameters",
trainer.parameters)
# print("*/*/*/*/*/*/*/*/*/*/*/*/*")
# print(trainer)
# print("*/*/*/*/*/*/*/*/*/*/*/*/*")
policy = trainer.create_policy(
env_manager.external_brains[name_behavior_id])
trainer.add_policy(name_behavior_id, policy)
agent_manager = AgentManager(
policy,
name_behavior_id,
trainer.stats_reporter,
trainer.parameters.get("time_horizon", sys.maxsize),
)
env_manager.set_agent_manager(name_behavior_id, agent_manager)
env_manager.set_policy(name_behavior_id, policy)
self.brain_name_to_identifier[brain_name].add(name_behavior_id)
trainer.publish_policy_queue(agent_manager.policy_queue)
trainer.subscribe_trajectory_queue(agent_manager.trajectory_queue)
def _create_trainer_and_manager(
self, env_manager: EnvManager, name_behavior_id: str
) -> None:
parsed_behavior_id = BehaviorIdentifiers.from_name_behavior_id(name_behavior_id)
brain_name = parsed_behavior_id.brain_name
try:
trainer = self.trainers[brain_name]
except KeyError:
trainer = self.trainer_factory.generate(brain_name)
self.trainers[brain_name] = trainer
policy = trainer.create_policy(
parsed_behavior_id, env_manager.external_brains[name_behavior_id]
)
trainer.add_policy(parsed_behavior_id, policy)
agent_manager = AgentManager(
policy,
name_behavior_id,
trainer.stats_reporter,
trainer.parameters.get("time_horizon", sys.maxsize),
)
env_manager.set_agent_manager(name_behavior_id, agent_manager)
env_manager.set_policy(name_behavior_id, policy)
self.brain_name_to_identifier[brain_name].add(name_behavior_id)
trainer.publish_policy_queue(agent_manager.policy_queue)
trainer.subscribe_trajectory_queue(agent_manager.trajectory_queue)
def _create_trainer_and_manager(self, env_manager: EnvManager,
name_behavior_id: str) -> None:
parsed_behavior_id = BehaviorIdentifiers.from_name_behavior_id(
name_behavior_id)
brain_name = parsed_behavior_id.brain_name
try:
trainer = self.trainers[brain_name]
except KeyError:
trainer = self.trainer_factory.generate(brain_name)
self.trainers[brain_name] = trainer
policy = trainer.create_policy(
parsed_behavior_id, env_manager.external_brains[name_behavior_id])
trainer.add_policy(parsed_behavior_id, policy)
agent_manager = AgentManager(
policy,
name_behavior_id,
trainer.stats_reporter,
trainer.parameters.get("time_horizon", sys.maxsize),
)
env_manager.set_agent_manager(name_behavior_id, agent_manager)
env_manager.set_policy(name_behavior_id, policy)
self.brain_name_to_identifier[brain_name].add(name_behavior_id)
trainer.publish_policy_queue(agent_manager.policy_queue)
trainer.subscribe_trajectory_queue(agent_manager.trajectory_queue)
if trainer.threaded:
# Start trainer thread
trainerthread = threading.Thread(target=self.trainer_update_func,
args=(trainer, ),
daemon=True)
trainerthread.start()
self.trainer_threads.append(trainerthread)
def _create_trainer_and_manager(
self, env_manager: EnvManager, name_behavior_id: str
) -> None:
parsed_behavior_id = BehaviorIdentifiers.from_name_behavior_id(name_behavior_id)
brain_name = parsed_behavior_id.brain_name
trainerthread = None
if brain_name in self.trainers:
trainer = self.trainers[brain_name]
else:
trainer = self.trainer_factory.generate(brain_name)
self.trainers[brain_name] = trainer
if trainer.threaded:
# Only create trainer thread for new trainers
trainerthread = threading.Thread(
target=self.trainer_update_func, args=(trainer,), daemon=True
)
self.trainer_threads.append(trainerthread)
env_manager.on_training_started(
brain_name, self.trainer_factory.trainer_config[brain_name]
)
policy = trainer.create_policy(
parsed_behavior_id,
env_manager.training_behaviors[name_behavior_id],
create_graph=True,
)
trainer.add_policy(parsed_behavior_id, policy)
agent_manager = AgentManager(
policy,
name_behavior_id,
trainer.stats_reporter,
trainer.parameters.time_horizon,
threaded=trainer.threaded,
)
env_manager.set_agent_manager(name_behavior_id, agent_manager)
env_manager.set_policy(name_behavior_id, policy)
self.brain_name_to_identifier[brain_name].add(name_behavior_id)
trainer.publish_policy_queue(agent_manager.policy_queue)
trainer.subscribe_trajectory_queue(agent_manager.trajectory_queue)
# Only start new trainers
if trainerthread is not None:
trainerthread.start()