  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 multi-intention IRL framework, there has been persistent
inquiry into inferring discrete time-varying 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.
