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110 行
4.4 KiB
110 行
4.4 KiB
from typing import Any, Dict
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from mlagents.trainers import MetaCurriculum
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from mlagents.envs.exception import UnityEnvironmentException
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from mlagents.trainers import Trainer
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from mlagents.envs.brain import BrainParameters
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from mlagents.trainers.ppo.trainer import PPOTrainer
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from mlagents.trainers.sac.trainer import SACTrainer
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from mlagents.trainers.bc.offline_trainer import OfflineBCTrainer
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from mlagents.trainers.bc.online_trainer import OnlineBCTrainer
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def initialize_trainers(
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trainer_config: Dict[str, Any],
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external_brains: Dict[str, BrainParameters],
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summaries_dir: str,
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run_id: str,
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model_path: str,
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keep_checkpoints: int,
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train_model: bool,
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load_model: bool,
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seed: int,
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meta_curriculum: MetaCurriculum = None,
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multi_gpu: bool = False,
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) -> Dict[str, Trainer]:
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"""
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Initializes trainers given a provided trainer configuration and set of brains from the environment, as well as
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some general training session options.
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:param trainer_config: Original trainer configuration loaded from YAML
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:param external_brains: BrainParameters provided by the Unity environment
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:param summaries_dir: Directory to store trainer summary statistics
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:param run_id: Run ID to associate with this training run
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:param model_path: Path to save the model
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:param keep_checkpoints: How many model checkpoints to keep
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:param train_model: Whether to train the model (vs. run inference)
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:param load_model: Whether to load the model or randomly initialize
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:param seed: The random seed to use
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:param meta_curriculum: Optional meta_curriculum, used to determine a reward buffer length for PPOTrainer
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:param multi_gpu: Whether to use multi-GPU training
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:return:
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"""
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trainers = {}
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trainer_parameters_dict = {}
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for brain_name in external_brains:
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trainer_parameters = trainer_config["default"].copy()
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trainer_parameters["summary_path"] = "{basedir}/{name}".format(
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basedir=summaries_dir, name=str(run_id) + "_" + brain_name
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)
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trainer_parameters["model_path"] = "{basedir}/{name}".format(
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basedir=model_path, name=brain_name
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)
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trainer_parameters["keep_checkpoints"] = keep_checkpoints
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if brain_name in trainer_config:
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_brain_key: Any = brain_name
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while not isinstance(trainer_config[_brain_key], dict):
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_brain_key = trainer_config[_brain_key]
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trainer_parameters.update(trainer_config[_brain_key])
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trainer_parameters_dict[brain_name] = trainer_parameters.copy()
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for brain_name in external_brains:
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if trainer_parameters_dict[brain_name]["trainer"] == "offline_bc":
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trainers[brain_name] = OfflineBCTrainer(
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external_brains[brain_name],
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trainer_parameters_dict[brain_name],
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train_model,
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load_model,
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seed,
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run_id,
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)
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elif trainer_parameters_dict[brain_name]["trainer"] == "online_bc":
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trainers[brain_name] = OnlineBCTrainer(
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external_brains[brain_name],
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trainer_parameters_dict[brain_name],
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train_model,
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load_model,
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seed,
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run_id,
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)
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elif trainer_parameters_dict[brain_name]["trainer"] == "ppo":
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trainers[brain_name] = PPOTrainer(
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external_brains[brain_name],
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meta_curriculum.brains_to_curriculums[brain_name].min_lesson_length
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if meta_curriculum
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else 1,
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trainer_parameters_dict[brain_name],
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train_model,
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load_model,
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seed,
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run_id,
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multi_gpu,
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)
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elif trainer_parameters_dict[brain_name]["trainer"] == "sac":
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trainers[brain_name] = SACTrainer(
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external_brains[brain_name],
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meta_curriculum.brains_to_curriculums[brain_name].min_lesson_length
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if meta_curriculum
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else 1,
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trainer_parameters_dict[brain_name],
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train_model,
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load_model,
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seed,
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run_id,
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)
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else:
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raise UnityEnvironmentException(
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"The trainer config contains "
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"an unknown trainer type for "
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"brain {}".format(brain_name)
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)
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return trainers
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