Abstract
Automated decision-making under uncertainty requires balancing exploitation and exploration. Classical methods treat these separately using heuristics, while Active Inference unifies them through Expected Free Energy (EFE) minimization. However, EFE minimization is computationally expensive, limiting scalability. We build on recent theory recasting EFE minimization as variational inference, formally unifying it with Planning-as-Inference and showing the epistemic drive as a unique entropic contribution. Our main contribution is a novel message-passing scheme for this unified objective, enabling scalable Active Inference in factored-state MDPs and overcoming high-dimensional planning intractability.
Wouter Nuijten
PhD Student
PhD student studying active inference as variational inference; core contributor to RxInfer.jl.
Mykola Lukashchuk
PhD student
I am a PhD candidate at the Electrical Engineering department, Eindhoven University of Technology.