def load_config(trainer_config_path):
try:
with open(trainer_config_path) as data_file:
trainer_config = yaml.load(data_file)
return trainer_config
except IOError:
raise UnityEnvironmentException('Parameter file could not be found '
'at {}.'.format(trainer_config_path))
except UnicodeDecodeError:
raise UnityEnvironmentException(
'There was an error decoding '
'Trainer Config from this path : {}'.format(trainer_config_path))
def _create_model_path(model_path):
try:
if not os.path.exists(model_path):
os.makedirs(model_path)
except Exception:
raise UnityEnvironmentException('The folder {} containing the '
'generated model could not be '
'accessed. Please make sure the '
'permissions are set correctly.'
.format(model_path))
def initialize_trainers(self, trainer_config):
"""
Initialization of the trainers
:param trainer_config: The configurations of the trainers
"""
trainer_parameters_dict = {}
for brain_name in self.external_brains:
trainer_parameters = trainer_config['default'].copy()
trainer_parameters['summary_path'] = '{basedir}/{name}'.format(
basedir=self.summaries_dir,
name=str(self.run_id) + '_' + brain_name)
trainer_parameters['model_path'] = '{basedir}/{name}'.format(
basedir=self.model_path,
name=brain_name)
trainer_parameters['keep_checkpoints'] = self.keep_checkpoints
if brain_name in trainer_config:
_brain_key = brain_name
while not isinstance(trainer_config[_brain_key], dict):
_brain_key = trainer_config[_brain_key]
for k in trainer_config[_brain_key]:
trainer_parameters[k] = trainer_config[_brain_key][k]
trainer_parameters_dict[brain_name] = trainer_parameters.copy()
for brain_name in self.external_brains:
if trainer_parameters_dict[brain_name]['trainer'] == 'offline_bc':
self.trainers[brain_name] = OfflineBCTrainer(
self.external_brains[brain_name],
trainer_parameters_dict[brain_name], self.train_model,
self.load_model, self.seed, self.run_id)
elif trainer_parameters_dict[brain_name]['trainer'] == 'online_bc':
self.trainers[brain_name] = OnlineBCTrainer(
self.external_brains[brain_name],
trainer_parameters_dict[brain_name], self.train_model,
self.load_model, self.seed, self.run_id)
elif trainer_parameters_dict[brain_name]['trainer'] == 'ppo':
self.trainers[brain_name] = PPOTrainer(
self.external_brains[brain_name],
self.meta_curriculum
.brains_to_curriculums[brain_name]
.min_lesson_length if self.meta_curriculum else 0,
trainer_parameters_dict[brain_name],
self.train_model, self.load_model, self.seed, self.run_id)
else:
raise UnityEnvironmentException('The trainer config contains '
'an unknown trainer type for '
'brain {}'
.format(brain_name))
def initialize_trainers(self, trainer_config):
"""
Initialization of the trainers
trainer_config:
The configurations of the trainers
"""
trainer_parameters_dict = {}
for brain_name in self.external_brains:
# brain_nameは "Leaner"
trainer_parameters = trainer_config['default'].copy()
trainer_parameters['summary_path'] = '{basedir}/{name}'.format(
basedir=self.summaries_dir,
name=str(self.run_id) + '_' + brain_name)
trainer_parameters['model_path'] = '{basedir}/{name}'.format(
basedir=self.model_path, name=brain_name)
trainer_parameters['keep_checkpoints'] = self.keep_checkpoints
if brain_name in trainer_config:
_brain_key = brain_name # "Learner"
while not isinstance(trainer_config[_brain_key], dict):
_brain_key = trainer_config[_brain_key]
for k in trainer_config[_brain_key]:
trainer_parameters[k] = trainer_config[_brain_key][k]
trainer_parameters_dict[brain_name] = trainer_parameters.copy()
for brain_name in self.external_brains:
if trainer_parameters_dict[brain_name]['trainer'] == 'ppo':
# ここで PPOTrainer 生成
self.trainers[brain_name] = PPOTrainer(
self.external_brains[brain_name],
0,
trainer_parameters_dict[
brain_name], # trainer_configで指定した内容
self.train_model,
self.load_model,
self.seed,
self.run_id)
else:
raise UnityEnvironmentException('The trainer config contains '
'an unknown trainer type for '
'brain {}'.format(brain_name))