您最多选择25个主题
主题必须以中文或者字母或数字开头，可以包含连字符 (-)，并且长度不得超过35个字符
75 行
2.6 KiB
75 行
2.6 KiB
# # Unity ML-Agents Toolkit
|
|
# ## ML-Agent Learning (Behavioral Cloning)
|
|
# Contains an implementation of Behavioral Cloning Algorithm
|
|
|
|
import logging
|
|
import copy
|
|
|
|
from mlagents.trainers.bc.trainer import BCTrainer
|
|
from mlagents.trainers.demo_loader import demo_to_buffer
|
|
from mlagents.trainers.trainer import UnityTrainerException
|
|
|
|
logger = logging.getLogger("mlagents.trainers")
|
|
|
|
|
|
class OfflineBCTrainer(BCTrainer):
|
|
"""The OfflineBCTrainer is an implementation of Offline Behavioral Cloning."""
|
|
|
|
def __init__(self, brain, trainer_parameters, training, load, seed, run_id):
|
|
"""
|
|
Responsible for collecting experiences and training PPO model.
|
|
:param trainer_parameters: The parameters for the trainer (dictionary).
|
|
:param training: Whether the trainer is set for training.
|
|
:param load: Whether the model should be loaded.
|
|
:param seed: The seed the model will be initialized with
|
|
:param run_id: The The identifier of the current run
|
|
"""
|
|
super(OfflineBCTrainer, self).__init__(
|
|
brain, trainer_parameters, training, load, seed, run_id
|
|
)
|
|
|
|
self.param_keys = [
|
|
"batch_size",
|
|
"summary_freq",
|
|
"max_steps",
|
|
"batches_per_epoch",
|
|
"use_recurrent",
|
|
"hidden_units",
|
|
"learning_rate",
|
|
"num_layers",
|
|
"sequence_length",
|
|
"memory_size",
|
|
"model_path",
|
|
"demo_path",
|
|
]
|
|
|
|
self.check_param_keys()
|
|
self.batches_per_epoch = trainer_parameters["batches_per_epoch"]
|
|
self.n_sequences = max(
|
|
int(trainer_parameters["batch_size"] / self.policy.sequence_length), 1
|
|
)
|
|
|
|
brain_params, self.demonstration_buffer = demo_to_buffer(
|
|
trainer_parameters["demo_path"], self.policy.sequence_length
|
|
)
|
|
|
|
policy_brain = copy.deepcopy(brain.__dict__)
|
|
expert_brain = copy.deepcopy(brain_params.__dict__)
|
|
policy_brain.pop("brain_name")
|
|
expert_brain.pop("brain_name")
|
|
if expert_brain != policy_brain:
|
|
raise UnityTrainerException(
|
|
"The provided demonstration is not compatible with the "
|
|
"brain being used for performance evaluation."
|
|
)
|
|
|
|
def __str__(self):
|
|
return """Hyperparameters for the Imitation Trainer of brain {0}: \n{1}""".format(
|
|
self.brain_name,
|
|
"\n".join(
|
|
[
|
|
"\t{0}:\t{1}".format(x, self.trainer_parameters[x])
|
|
for x in self.param_keys
|
|
]
|
|
),
|
|
)
|