Efficient search of active inference policy spaces using k-means

Alex B. Kiefer; Mahault Albarracin

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arXiv · 2209.02550

Efficient search of active inference policy spaces using k-means

Alex B. Kiefer·Mahault Albarracin

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Abstract

We develop an approach to policy selection in active inference that allows us to efficiently search large policy spaces by mapping each policy to its embedding in a vector space. We sample the expected free energy of representative points in the space, then perform a more thorough policy search around the most promising point in this initial sample. We consider various approaches to creating the policy embedding space, and propose using k-means clustering to select representative points. We apply our technique to a goal-oriented graph-traversal problem, for which naive policy selection is intractable for even moderately large graphs.

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Alex B. Kiefer, Mahault Albarracin. 2022-09-06. Efficient search of active inference policy spaces using k-means. https://arxiv.org/abs/2209.02550

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