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Behavioral Cloning from Observation.
Faraz Torabi, Garrett
Warnell, and Peter Stone.
In Proceedings of the 27th International
Joint Conference on Artificial Intelligence (IJCAI), July 2018.
Also available from arXiv
[PDF]2.3MB [slides.pptx]26.0MB
Humans often learn how to perform tasks via imitation: they observe others perform a task, and then very quickly infer the appropriate actions to take based on their observations. While extending this paradigm to autonomous agents is a well-studied problem in general, there are two particular aspects that have largely been overlooked: (1) that the learning is done from observation only (i.e., without explicit action information), and (2) that the learning is typically done very quickly. In this work, we propose a two-phase, autonomous imitation learning technique called behavioral cloning from observation (BCO), that aims to provide improved performance with respect to both of these aspects. First, we allow the agent to acquire experience in a self-supervised fashion. This experience is used to develop a model which is then utilized to learn a particular task by observing an expert perform that task without the knowledge of the specific actions taken. We experimentally compare BCO to imitation learning methods, including the state-of-the-art, generative adversarial imitation learning (GAIL) technique, and we show comparable task performance in several different simulation domains while exhibiting increased learning speed after expert trajectories become available.
@InProceedings{IJCAI2018-torabi,
author = {Faraz Torabi and Garrett Warnell and Peter Stone},
title = {Behavioral Cloning from Observation},
booktitle = {Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI)},
location = {Stockholm, Sweden},
month = {July},
year = {2018},
abstract = {
Humans often learn how to perform tasks via imitation: they observe others
perform a task, and then very quickly infer the appropriate actions to take
based on their observations. While extending this paradigm to autonomous
agents is a well-studied problem in general, there are two particular aspects
that have largely been overlooked: (1) that the learning is done from
observation only (i.e., without explicit action information), and (2) that
the learning is typically done very quickly. In this work, we propose a
two-phase, autonomous imitation learning technique called behavioral cloning
from observation (BCO), that aims to provide improved performance with respect
to both of these aspects. First, we allow the agent to acquire experience in a
self-supervised fashion. This experience is used to develop a model which is
then utilized to learn a particular task by observing an expert perform that
task without the knowledge of the specific actions taken. We experimentally
compare BCO to imitation learning methods, including the state-of-the-art,
generative adversarial imitation learning (GAIL) technique, and we show
comparable task performance in several different simulation domains while
exhibiting increased learning speed after expert trajectories become available.
},
wwwnote={Also available from <a href="https://arxiv.org/abs/1805.01954">arXiv</a>},
}
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