L(M)V-IQL: Multiple Intention Inverse Reinforcement Learning for Animal Behavior Characterization.
CoRR(2023)
摘要
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.
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