Title: Distributional Inverse Reinforcement Learning
Primary area: reinforcement learning
Keywords: Inverse Reinforcement Learning, Neuroscience, Reinforcement Learning, Robotics

Abstract: We propose a distributional framework for offline Inverse Reinforcement Learning (IRL) that jointly models uncertainty over reward functions and full distributions of returns. Unlike conventional IRL approaches that recover a deterministic reward estimate or match only expected returns, our method captures richer structure in expert behavior, particularly in learning the reward distribution, by minimizing first-order stochastic dominance (FSD) violations and thus integrating distortion risk measures (DRMs) into policy learning, enabling the recovery of both reward distributions and distribution-aware policies. This formulation is well-suited for behavior analysis and risk-aware imitation learning. Empirical results on synthetic benchmarks, real-world neurobehavioral data, and MuJoCo control tasks demonstrate that our method recovers expressive reward representations and achieves state-of-the-art imitation performance.

TL;DR: We develop a distributional offline IRL framework that infers reward distributions and risk-sensitive policies via stochastic dominance and distortion risk measures, enabling state-of-the-art performance on synthetic, neural, and MuJoCo benchmarks.