Learning Utilities from Demonstrations in Markov Decision Processes

ICLR 2025 Conference Submission4728 Authors

Keywords: Inverse Reinforcement Learning, Risk, Imitation Learning, Theory

TL;DR: We introduce and analyze Utility Learning, as the problem of inferring the risk attitude of an agent from demonstrations of behavior.

Abstract: Our goal is to extract useful knowledge from demonstrations of behavior in sequential decision-making problems. Although it is well-known that humans commonly engage in *risk-sensitive* behaviors in the presence of stochasticity, most Inverse Reinforcement Learning (IRL) models assume a *risk-neutral* agent. Beyond introducing model misspecification, these models do not directly capture the risk attitude of the observed agent, which can be crucial in many applications. In this paper, we propose a novel model of behavior in Markov Decision Processes (MDPs) that explicitly represents the agent's risk attitude through a *utility* function. We then define the Utility Learning (UL) problem as the task of inferring the observed agent's risk attitude, encoded via a utility function, from demonstrations in MDPs, and we analyze the partial identifiability of the agent's utility. Furthermore, we devise two provably efficient algorithms for UL in a finite-data regime, and we analyze their sample complexity. We conclude with proof-of-concept experiments that empirically validate both our model and our algorithms.

Primary Area: reinforcement learning

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Submission Number: 4728