  Deep active inference has been proposed as a scalable approach to perception
and action that deals with large policy and state spaces. However, current
models are limited to fully observable domains. In this paper, we describe a
deep active inference model that can learn successful policies directly from
high-dimensional sensory inputs. The deep learning architecture optimizes a
variant of the expected free energy and encodes the continuous state
representation by means of a variational autoencoder. We show, in the OpenAI
benchmark, that our approach has comparable or better performance than deep
Q-learning, a state-of-the-art deep reinforcement learning algorithm.
