Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods
This addresses the limitation of assuming risk neutrality in IRL for applications like autonomous driving, though it is incremental as it extends existing IRL methods to incorporate risk sensitivity.
The paper tackled the problem of modeling human risk sensitivity in Inverse Reinforcement Learning (IRL) by proposing a framework based on coherent risk measures, which was demonstrated on a simulated driving game with ten participants, showing it could infer and mimic diverse driving styles more accurately than risk-neutral models, especially in scenarios with catastrophic outcomes like collisions.
The literature on Inverse Reinforcement Learning (IRL) typically assumes that humans take actions in order 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 IRL in order 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 ten 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 Risk-Sensitive (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.