Modeling and Learning Intent-Driven Expert Behavior for Sequential Decision-Making Tasks

This paper introduces IDIL, a novel imitation learning algorithm that can effectively model and learn intent-driven expert behavior in sequential decision-making tasks. IDIL is capable of capturing the diversity in expert behaviors arising from differences in their intents, even when the intents are unobservable.