L(M)V-IQL: Multiple Intention Inverse Reinforcement Learning for Animal Behavior Characterization

In advancing the understanding of decision-making processes, mathematical models, particularly Inverse Reinforcement Learning (IRL), have proven instrumental in reconstructing animal's multiple intentions amidst complex behaviors. Given the recent development of a continuous-time multi-intention IRL framework, there has been persistent inquiry into inferring discrete time-varying reward functions with multiple intention IRL approaches. To tackle the challenge, we introduce the Latent (Markov) Variable Inverse Q-learning (L(M)V-IQL) algorithms, a novel IRL framework tailored for accommodating discrete intrinsic rewards. Leveraging an Expectation-Maximization approach, we cluster observed trajectories into distinct intentions and independently solve the IRL problem for each. Demonstrating the efficacy of L(M)V-IQL through simulated experiments and its application to different real mouse behavior datasets, our approach surpasses current benchmarks in animal behavior prediction, producing interpretable reward functions. This advancement holds promise for neuroscience and psychology, contributing to a deeper understanding of animal decision-making and uncovering underlying brain mechanisms.