Quantifying Potential Observation Missingness in Inverse Reinforcement Learning

ArXi:2605.12831v1 Announce Type: new Inverse reinforcement learning (IRL), which infers reward functions from nstrations, is a valuable tool for modeling and understanding decision-making behavior. Many variants of IRL have been developed to capture complexities of human decision-making, such as subjective beliefs, imperfect planning, and dynamic goals. However, an often-overlooked issue in real-world behavioral datasets is that the recorded data may be missing observations that were available to the original decision-maker.