Distributional Inverse Reinforcement Learning

• URL: http://arxiv.org/abs/2510.03013v2
• Date: Mon, 06 Oct 2025 12:56:00 GMT
• Title: Distributional Inverse Reinforcement Learning
• Authors: Feiyang Wu, Ye Zhao, Anqi Wu,
• Abstract summary: We propose a distributional framework for offline Inverse Reinforcement Learning (IRL)<n>Our method captures structure in expert behavior, particularly in learning the reward distribution.<n>This formulation is well-suited for behavior analysis and risk-aware imitation learning.
• Score: 12.590471116307485
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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