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arXiv · 2508.02197

A Message Passing Realization of Expected Free Energy Minimization

Wouter W. L. Nuijten·Mykola Lukashchuk·Thijs van de Laar·Bert de Vries
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Abstract

We present a message passing approach to Expected Free Energy (EFE) minimization on factor graphs, based on the theory introduced in arXiv:2504.14898. By reformulating EFE minimization as Variational Free Energy minimization with epistemic priors, we transform a combinatorial search problem into a tractable inference problem solvable through standard variational techniques. Applying our message passing method to factorized state-space models enables efficient policy inference. We evaluate our method on environments with epistemic uncertainty: a stochastic gridworld and a partially observable Minigrid task. Agents using our approach consistently outperform conventional KL-control agents on these tasks, showing more robust planning and efficient exploration under uncertainty. In the stochastic gridworld environment, EFE-minimizing agents avoid risky paths, while in the partially observable minigrid setting, they conduct more systematic information-seeking. This approach bridges active inference theory with practical implementations, providing empirical evidence for the efficiency of epistemic priors in artificial agents.

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Wouter W. L. Nuijten, Mykola Lukashchuk, Thijs van de Laar, Bert de Vries. 2025-08-04. A Message Passing Realization of Expected Free Energy Minimization. https://doi.org/10.1007/978-3-032-16955-6_5

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