Abstract
We present a message passing approach to Expected Free Energy (EFE) minimization on factor graphs, based on the theory introduced in (De Vries, 2025). 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.
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.
Thijs van de Laar
Assistant professor
I am an assisant professor at BIASlab, where I work on artificial agents that learn to control themselves in uncertain environments. I take inspiration from physics and neuroscience, and develop theory and (software) tools that allow for efficient, real-time interaction.
