International Journal of Intelligent Computing and Cybernetics A survey of inverse reinforcement learning techniques) "A survey of inverse reinforcement learning techniques A survey of inverse reinforcement learning techniques

Abstract

Access to this document was granted through an Emerald subscription provided by emerald-srm:461813 [] For Authors If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.com Emerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services. 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.