Load curriculum from same YAML

ml-agents/mlagents/trainers/learn.py

@@ -16,6 +16,7 @@
  load_config,
  TrainerFactory,
  handle_existing_directories,
+ assemble_curriculum_config,
 )
 from mlagents.trainers.stats import (
  TensorboardWriter,
@@ -50,18 +51,6 @@ def _create_parser():
  argparser.add_argument(
  "--env", default=None, dest="env_path", help="Name of the Unity executable "
  )
- argparser.add_argument(
- "--curriculum",
- default=None,
- dest="curriculum_config_path",
- help="Curriculum config yaml file for environment",
- )
- argparser.add_argument(
- "--sampler",
- default=None,
- dest="sampler_file_path",
- help="Reset parameter yaml file for environment",
- )
  argparser.add_argument(
  "--keep-checkpoints",
  default=5,
@@ -239,24 +228,22 @@ def from_argparse(args: argparse.Namespace) -> "RunOptions":
  """
  argparse_args = vars(args)
  config_path = argparse_args["trainer_config_path"]
- curriculum_config_path = argparse_args["curriculum_config_path"]
  full_config = load_config(config_path)
  try:
  argparse_args["trainer_config"] = full_config["behaviors"]
  except KeyError:
  raise TrainerConfigError(
  "Trainer configurations not found. Make sure your YAML file has a section for behaviors."
  )
- if curriculum_config_path is not None:
- argparse_args["curriculum_config"] = load_config(curriculum_config_path)
+ curriculum_config = assemble_curriculum_config(argparse_args["trainer_config"])
+ if len(curriculum_config) > 0:
+ argparse_args["curriculum_config"] = curriculum_config
  if "parameter_randomization" in full_config:
  argparse_args["sampler_config"] = full_config["parameter_randomization"]
  # Keep deprecated --load working, TODO: remove
  argparse_args["resume"] = argparse_args["resume"] or argparse_args["load_model"]
  # Since argparse accepts file paths in the config options which don't exist in CommandLineOptions,
  # these keys will need to be deleted to use the **/splat operator below.
- argparse_args.pop("sampler_file_path")
- argparse_args.pop("curriculum_config_path")
  argparse_args.pop("trainer_config_path")
  return RunOptions(**vars(args))