Epistemic Value Anticipation into the Deep Active Inference Model

Abstract

This article introduces a novel mathematical and computational framework for epistemic value calculation within deep active inference models. We focus on a visual foraging problem in a static environment, using toy and real-world MNIST datasets to explore the drawbacks and advantages of the standard method compared to our proposed approach. Testing with relevant metrics, our approach demonstrates improved results in the considered scenarios.