Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods

TL;DR

This paper develops a risk-sensitive inverse reinforcement learning framework using semi- and non-parametric methods, enabling the inference of human risk preferences from decision-making data, demonstrated through a simulated driving game.

Contribution

It introduces a flexible risk-sensitive IRL framework based on coherent risk measures and efficient algorithms for inferring human risk preferences in static and dynamic settings.

Findings

Successfully infers diverse human risk attitudes from data

More accurately models behavior in risky scenarios than risk-neutral IRL

Demonstrates effectiveness in a simulated driving task

Abstract

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.