Learning Perception and Planning with Deep Active Inference

LEARNING PERCEPTION AND PLANNING WITH DEEP ACTIVE INFERENCE Ozan C atal Tim V erbelen Johannes Nauta Cedric De Boom Bart Dhoedt IDLab Department of Information Technology at Ghent University - imec ABSTRACT Active inference is a process theory of the brain that states that all living organisms infer actions in order to minimize their (expected) free energy. However, current experiments are limited to predefined, often discrete, state spaces. In this paper we use recent advances in deep learning to learn the state space and approximate the necessary probability distributions to engage in active inference. Index T erms -- active inference, deep learning, perception, planning 1. INTRODUCTION Active inference postulates that action selection in biological systems, in particular the human brain, is actually an inference problem where agents are attracted to a preferred prior state distribution in a hidden state space [1]. To do so, each living organism builds an internal generative model of the world, by minimizing the so-called free energy.