Skip to main content

Risk-sensitive inverse reinforcement learning via semi- and non-parametric methods

Author(s): Singh, S; Lacotte, J; Majumdar, Anirudha; Pavone, M

To refer to this page use:
Abstract: The literature on inverse reinforcement learning (IRL) typically assumes that humans take actions to minimize the expected value of a cost function, i.e., that humans are risk neutral. Yet, in practice, humans are often far from being risk neutral. To fill this gap, the objective of this paper is to devise a framework for risk-sensitive (RS) IRL to explicitly account for a human’s risk sensitivity. To this end, we propose a flexible class of models based on coherent risk measures, which allow us to capture an entire spectrum of risk preferences from risk neutral to worst case. We propose efficient non-parametric algorithms based on linear programming and semi-parametric algorithms based on maximum likelihood for inferring a human’s underlying risk measure and cost function for a rich class of static and dynamic decision-making settings. The resulting approach is demonstrated on a simulated driving game with 10 human participants. Our method is able to infer and mimic a wide range of qualitatively different driving styles from highly risk averse to risk neutral in a data-efficient manner. Moreover, comparisons of the RS-IRL approach with a risk-neutral model show that the RS-IRL framework more accurately captures observed participant behavior both qualitatively and quantitatively, especially in scenarios where catastrophic outcomes such as collisions can occur.
Publication Date: 2018
Citation: Singh, S, Lacotte, J, Majumdar, A, Pavone, M. (2018). Risk-sensitive inverse reinforcement learning via semi- and non-parametric methods. International Journal of Robotics Research, 37 (1713 - 1740. doi:10.1177/0278364918772017
DOI: doi:10.1177/0278364918772017
Pages: 1713 - 1740
Type of Material: Journal Article
Journal/Proceeding Title: International Journal of Robotics Research
Version: Author's manuscript

Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.