In advancing the understanding of natural decision-making processes, inverse reinforcement
learning (IRL) methods have proven instrumental in reconstructing animal’s intentions underlying
complex behaviors. Given the recent development of a continuous-time multiintention
IRL framework, there has been persistent inquiry into inferring discrete timevarying
rewards with IRL. To address this challenge, we introduce the class of hierarchical
inverse Q-learning (HIQL) algorithms. Through an unsupervised learning process, HIQL
divides expert trajectories into multiple intention segments, and solves the IRL problem
independently for each. Applying HIQL to simulated experiments and several real animal
behavior datasets, our approach outperforms current benchmarks in behavior prediction
and produces interpretable reward functions. Our results suggest that the intention transition
dynamics underlying complex decision-making behavior is better modeled by a step
function instead of a smoothly varying function. This advancement holds promise for neuroscience
and cognitive science, contributing to a deeper understanding of decision-making
and uncovering underlying brain mechanisms.