  Active Inference is a theory of action arising from neuroscience which casts
action and planning as a bayesian inference problem to be solved by minimizing
a single quantity - the variational free energy. Active Inference promises a
unifying account of action and perception coupled with a biologically plausible
process theory. Despite these potential advantages, current implementations of
Active Inference can only handle small, discrete policy and state-spaces and
typically require the environmental dynamics to be known. In this paper we
propose a novel deep Active Inference algorithm which approximates key
densities using deep neural networks as flexible function approximators, which
enables Active Inference to scale to significantly larger and more complex
tasks. We demonstrate our approach on a suite of OpenAIGym benchmark tasks and
obtain performance comparable with common reinforcement learning baselines.
Moreover, our algorithm shows similarities with maximum entropy reinforcement
learning and the policy gradients algorithm, which reveals interesting
connections between the Active Inference framework and reinforcement learning.
