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Generative Adversarial Imitation from Observation.
Faraz Torabi,
Garrett Warnell, and Peter
Stone.
In Imitation, Intent, and Interaction (I3) Workshop at ICML 2019, June 2019.
Imitation from observation (IfO) is the problem of learning directly from state-only demonstrations without having access to the demonstrator's actions.The lack of action information both distinguishes IfO from most of the literature in imitation learning, and also sets it apart as a method that may enable agents to learn from a large set of previously inapplicable resources such as internet videos. In this paper, we propose both a general framework for IfO approaches and also a new IfO approach based on generative adversarial networks called generative adversarial imitation from observation (GAIfO). We conduct experiments in two different settings: (1) when demonstrations consist of low-dimensional, manually-defined state features, and (2) when demonstrations consist of high-dimensional, raw visual data. We demonstrate that our approach performs comparably to classical imitation learning approaches (which have access to the demonstrator's actions) and significantly outperforms existing imitation from observation methods in high-dimensional simulation environments.
@InProceedings{ICML19a-torabi,
author = {Faraz Torabi and Garrett Warnell and Peter Stone},
title = {Generative Adversarial Imitation from Observation},
booktitle = {Imitation, Intent, and Interaction (I3) Workshop at ICML 2019},
location = {Long Beach, California, USA},
month = {June},
year = {2019},
abstract = {
Imitation from observation (IfO) is the problem of learning directly from
state-only demonstrations without having access to the demonstrator's actions.
The lack of action information both distinguishes IfO from most of the
literature in imitation learning, and also sets it apart as a method that may
enable agents to learn from a large set of previously inapplicable resources
such as internet videos. In this paper, we propose both a general framework
for IfO approaches and also a new IfO approach based on generative adversarial
networks called generative adversarial imitation from observation (GAIfO). We
conduct experiments in two different settings: (1) when demonstrations consist
of low-dimensional, manually-defined state features, and (2) when
demonstrations consist of high-dimensional, raw visual data. We demonstrate
that our approach performs comparably to classical imitation learning
approaches (which have access to the demonstrator's actions) and significantly
outperforms existing imitation from observation methods in high-dimensional
simulation environments.
},
}
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