リスク感受性を考慮した逆強化学習：半パラメトリックおよびノンパラメトリック手法
Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods
人間のリスク選好を明示的に扱うため、整合的リスク尺度に基づく逆強化学習の枠組みを提案し、運転ゲーム実験で多様な運転スタイルを効率的に推定・模倣できることを示した。
著者: Sumeet Singh, Jonathan Lacotte, Anirudha Majumdar, Marco Pavone
分類: cs.AI, cs.LG, cs.RO
原文アブストラクト
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.
