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A survey of inverse reinforcement learning techniques

A survey of inverse reinforcement learning techniques Purpose – This purpose of this paper is to provide an overview of the theoretical background and applications of inverse reinforcement learning (IRL). Design/methodology/approach – Reinforcement learning (RL) techniques provide a powerful solution for sequential decision making problems under uncertainty. RL uses an agent equipped with a reward function to find a policy through interactions with a dynamic environment. However, one major assumption of existing RL algorithms is that reward function, the most succinct representation of the designer's intention, needs to be provided beforehand. In practice, the reward function can be very hard to specify and exhaustive to tune for large and complex problems, and this inspires the development of IRL, an extension of RL, which directly tackles this problem by learning the reward function through expert demonstrations. In this paper, the original IRL algorithms and its close variants, as well as their recent advances are reviewed and compared. Findings – This paper can serve as an introduction guide of fundamental theory and developments, as well as the applications of IRL. Originality/value – This paper surveys the theories and applications of IRL, which is the latest development of RL and has not been done so far. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Intelligent Computing and Cybernetics Emerald Publishing

A survey of inverse reinforcement learning techniques

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Publisher
Emerald Publishing
Copyright
Copyright © 2012 Emerald Group Publishing Limited. All rights reserved.
ISSN
1756-378X
DOI
10.1108/17563781211255862
Publisher site
See Article on Publisher Site

Abstract

Purpose – This purpose of this paper is to provide an overview of the theoretical background and applications of inverse reinforcement learning (IRL). Design/methodology/approach – Reinforcement learning (RL) techniques provide a powerful solution for sequential decision making problems under uncertainty. RL uses an agent equipped with a reward function to find a policy through interactions with a dynamic environment. However, one major assumption of existing RL algorithms is that reward function, the most succinct representation of the designer's intention, needs to be provided beforehand. In practice, the reward function can be very hard to specify and exhaustive to tune for large and complex problems, and this inspires the development of IRL, an extension of RL, which directly tackles this problem by learning the reward function through expert demonstrations. In this paper, the original IRL algorithms and its close variants, as well as their recent advances are reviewed and compared. Findings – This paper can serve as an introduction guide of fundamental theory and developments, as well as the applications of IRL. Originality/value – This paper surveys the theories and applications of IRL, which is the latest development of RL and has not been done so far.

Journal

International Journal of Intelligent Computing and CyberneticsEmerald Publishing

Published: Aug 17, 2012

Keywords: Inverse reinforcement learning; Reward function; Reinforcement learning; Artificial intelligence; Learning methods

References

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