  This work combines the free energy principle from cognitive neuroscience and
the ensuing active inference dynamics with recent advances in variational
inference in deep generative models, and evolution strategies to introduce the
"deep active inference" agent. This agent minimises a variational free energy
bound on the average surprise of its sensations, which is motivated by a
homeostatic argument. It does so by optimising the parameters of a generative
latent variable model of its sensory inputs, together with a variational
density approximating the posterior distribution over the latent variables,
given its observations, and by acting on its environment to actively sample
input that is likely under this generative model. The internal dynamics of the
agent are implemented using deep and recurrent neural networks, as used in
machine learning, making the deep active inference agent a scalable and very
flexible class of active inference agent. Using the mountain car problem, we
show how goal directed behaviour can be implemented by defining appropriate
priors on the latent states in the agent's model. Furthermore, we show that the
deep active inference agent can learn a generative model of the environment,
which can be sampled from to understand the agent's beliefs about the
environment and its interaction therewith.
