  In this paper, we present a Bayesian view on model-based reinforcement
learning. We use expert knowledge to impose structure on the transition model
and present an efficient learning scheme based on variational inference. This
scheme is applied to a heteroskedastic and bimodal benchmark problem on which
we compare our results to NFQ and show how our approach yields
human-interpretable insight about the underlying dynamics while also increasing
data-efficiency.
