  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.
