Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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import os
import yaml
from typing import Any, Dict, TextIO
import logging
from mlagents.trainers.meta_curriculum import MetaCurriculum
from mlagents.trainers.exception import TrainerConfigError
from mlagents.trainers.trainer import Trainer
from mlagents.trainers.exception import UnityTrainerException
from mlagents.trainers.ppo.trainer import PPOTrainer
from mlagents.trainers.sac.trainer import SACTrainer
from mlagents.trainers.ghost.trainer import GhostTrainer
logger = logging.getLogger("mlagents.trainers")
class TrainerFactory:
def __init__(
self,
trainer_config: Any,
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,
):
self.trainer_config = trainer_config
self.summaries_dir = summaries_dir
self.run_id = run_id
self.model_path = model_path
self.keep_checkpoints = keep_checkpoints
self.train_model = train_model
self.load_model = load_model
self.seed = seed
self.meta_curriculum = meta_curriculum
self.multi_gpu = multi_gpu
def generate(self, brain_name: str) -> Trainer:
return initialize_trainer(
self.trainer_config,
brain_name,
self.summaries_dir,
self.run_id,
self.model_path,
self.keep_checkpoints,
self.train_model,
self.load_model,
self.seed,
self.meta_curriculum,
self.multi_gpu,
)
def initialize_trainer(
trainer_config: Any,
brain_name: str,
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,
) -> Trainer:
"""
Initializes a trainer given a provided trainer configuration and brain parameters, as well as
some general training session options.
:param trainer_config: Original trainer configuration loaded from YAML
:param brain_name: Name of the brain to be associated with trainer
: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
:return:
"""
if "default" not in trainer_config and brain_name not in trainer_config:
raise TrainerConfigError(
f'Trainer config must have either a "default" section, or a section for the brain name ({brain_name}). '
"See config/trainer_config.yaml for an example."
)
trainer_parameters = trainer_config.get("default", {}).copy()
trainer_parameters["summary_path"] = 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])
min_lesson_length = 1
if meta_curriculum:
if brain_name in meta_curriculum.brains_to_curricula:
min_lesson_length = meta_curriculum.brains_to_curricula[
brain_name
].min_lesson_length
else:
logger.warning(
f"Metacurriculum enabled, but no curriculum for brain {brain_name}. "
f"Brains with curricula: {meta_curriculum.brains_to_curricula.keys()}. "
)
trainer: Trainer = None # type: ignore # will be set to one of these, or raise
if "trainer" not in trainer_parameters:
raise TrainerConfigError(
f'The "trainer" key must be set in your trainer config for brain {brain_name} (or the default brain).'
)
trainer_type = trainer_parameters["trainer"]
if trainer_type == "offline_bc":
raise UnityTrainerException(
"The offline_bc trainer has been removed. To train with demonstrations, "
"please use a PPO or SAC trainer with the GAIL Reward Signal and/or the "
"Behavioral Cloning feature enabled."
)
elif trainer_type == "ppo":
trainer = PPOTrainer(
brain_name,
min_lesson_length,
trainer_parameters,
train_model,
load_model,
seed,
run_id,
)
elif trainer_type == "sac":
trainer = SACTrainer(
brain_name,
min_lesson_length,
trainer_parameters,
train_model,
load_model,
seed,
run_id,
)
else:
raise TrainerConfigError(
f'The trainer config contains an unknown trainer type "{trainer_type}" for brain {brain_name}'
)
if "self_play" in trainer_parameters:
trainer = GhostTrainer(
trainer,
brain_name,
min_lesson_length,
trainer_parameters,
train_model,
run_id,
)
return trainer
def load_config(config_path: str) -> Dict[str, Any]:
try:
with open(config_path) as data_file:
return _load_config(data_file)
except IOError:
abs_path = os.path.abspath(config_path)
raise TrainerConfigError(f"Config file could not be found at {abs_path}.")
except UnicodeDecodeError:
raise TrainerConfigError(
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 TrainerConfigError(
"Error parsing yaml file. Please check for formatting errors. "
"A tool such as http://www.yamllint.com/ can be helpful with this."
) from e