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import yaml
from typing import Any, Dict, TextIO
from mlagents.trainers.meta_curriculum import MetaCurriculum
from mlagents.envs.exception import UnityEnvironmentException
from mlagents.trainers.trainer import Trainer
from mlagents.envs.brain import BrainParameters
from mlagents.trainers.ppo.trainer import PPOTrainer
from mlagents.trainers.sac.trainer import SACTrainer
from mlagents.trainers.bc.offline_trainer import OfflineBCTrainer
def initialize_trainers(
trainer_config: Dict[str, Any],
external_brains: Dict[str, BrainParameters],
summaries_dir: str,
run_id: str,
model_path: str,
keep_checkpoints: int,
train_model: bool,
load_model: bool,
seed: int,
meta_curriculum: MetaCurriculum = None,
multi_gpu: bool = False,
) -> Dict[str, Trainer]:
"""
Initializes trainers given a provided trainer configuration and set of brains from the environment, as well as
some general training session options.
:param trainer_config: Original trainer configuration loaded from YAML
:param external_brains: BrainParameters provided by the Unity environment
:param summaries_dir: Directory to store trainer summary statistics
:param run_id: Run ID to associate with this training run
:param model_path: Path to save the model
:param keep_checkpoints: How many model checkpoints to keep
:param train_model: Whether to train the model (vs. run inference)
:param load_model: Whether to load the model or randomly initialize
:param seed: The random seed to use
:param meta_curriculum: Optional meta_curriculum, used to determine a reward buffer length for PPOTrainer
:param multi_gpu: Whether to use multi-GPU training
:return:
"""
trainers: Dict[str, Trainer] = {}
trainer_parameters_dict = {}
for brain_name in external_brains:
trainer_parameters = trainer_config["default"].copy()
trainer_parameters["summary_path"] = "{basedir}/{name}".format(
basedir=summaries_dir, name=str(run_id) + "_" + brain_name
)
trainer_parameters["model_path"] = "{basedir}/{name}".format(
basedir=model_path, name=brain_name
)
trainer_parameters["keep_checkpoints"] = keep_checkpoints
if brain_name in trainer_config:
_brain_key: Any = brain_name
while not isinstance(trainer_config[_brain_key], dict):
_brain_key = trainer_config[_brain_key]
trainer_parameters.update(trainer_config[_brain_key])
trainer_parameters_dict[brain_name] = trainer_parameters.copy()
for brain_name in external_brains:
if trainer_parameters_dict[brain_name]["trainer"] == "offline_bc":
trainers[brain_name] = OfflineBCTrainer(
external_brains[brain_name],
trainer_parameters_dict[brain_name],
train_model,
load_model,
seed,
run_id,
)
elif trainer_parameters_dict[brain_name]["trainer"] == "ppo":
trainers[brain_name] = PPOTrainer(
external_brains[brain_name],
meta_curriculum.brains_to_curriculums[brain_name].min_lesson_length
if meta_curriculum
else 1,
trainer_parameters_dict[brain_name],
train_model,
load_model,
seed,
run_id,
multi_gpu,
)
elif trainer_parameters_dict[brain_name]["trainer"] == "sac":
trainers[brain_name] = SACTrainer(
external_brains[brain_name],
meta_curriculum.brains_to_curriculums[brain_name].min_lesson_length
if meta_curriculum
else 1,
trainer_parameters_dict[brain_name],
train_model,
load_model,
seed,
run_id,
)
else:
raise UnityEnvironmentException(
"The trainer config contains "
"an unknown trainer type for "
"brain {}".format(brain_name)
)
return trainers
def load_config(config_path: str) -> Dict[str, Any]:
try:
with open(config_path) as data_file:
return _load_config(data_file)
except IOError:
raise UnityEnvironmentException(
f"Config file could not be found at {config_path}."
)
except UnicodeDecodeError:
raise UnityEnvironmentException(
f"There was an error decoding Config file from {config_path}. "
f"Make sure your file is save using UTF-8"
)
def _load_config(fp: TextIO) -> Dict[str, Any]:
"""
Load the yaml config from the file-like object.
"""
try:
return yaml.safe_load(fp)
except yaml.parser.ParserError as e:
raise UnityEnvironmentException(
"Error parsing yaml file. Please check for formatting errors. "
"A tool such as http://www.yamllint.com/ can be helpful with this."
) from e