def advance(self, env: EnvManager) -> int:
# Get steps
with hierarchical_timer("env_step"):
num_steps = env.advance()
# Report current lesson
if self.meta_curriculum:
for brain_name, curr in self.meta_curriculum.brains_to_curricula.items(
):
if brain_name in self.trainers:
self.trainers[brain_name].stats_reporter.set_stat(
"Environment/Lesson", curr.lesson_num)
# Advance trainers. This can be done in a separate loop in the future.
with hierarchical_timer("trainer_advance"):
for trainer in self.trainers.values():
trainer.advance()
return num_steps
def advance(self, env: EnvManager) -> int:
# Get steps
with hierarchical_timer("env_step"):
num_steps = env.advance()
# Report current lesson
if self.meta_curriculum:
for brain_name, curr in self.meta_curriculum.brains_to_curricula.items(
):
if brain_name in self.trainers:
self.trainers[brain_name].stats_reporter.set_stat(
"Environment/Lesson", curr.lesson_num)
for trainer in self.trainers.values():
if not trainer.threaded:
with hierarchical_timer("trainer_advance"):
trainer.advance()
return num_steps
def advance(self, env: EnvManager) -> int:
# Get steps
with hierarchical_timer("env_step"):
num_steps = env.advance()
# Report current lesson for each environment parameter
for (
param_name,
lesson_number,
) in self.param_manager.get_current_lesson_number().items():
for trainer in self.trainers.values():
trainer.stats_reporter.set_stat(
f"Environment/Lesson/{param_name}", lesson_number)
for trainer in self.trainers.values():
if not trainer.threaded:
with hierarchical_timer("trainer_advance"):
trainer.advance()
return num_steps