  Reinforcement Learning is divided in two main paradigms: model-free and
model-based. Each of these two paradigms has strengths and limitations, and has
been successfully applied to real world domains that are appropriate to its
corresponding strengths. In this paper, we present a new approach aimed at
bridging the gap between these two paradigms. We aim to take the best of the
two paradigms and combine them in an approach that is at the same time
data-efficient and cost-savvy. We do so by learning a probabilistic dynamics
model and leveraging it as a prior for the intertwined model-free optimization.
As a result, our approach can exploit the generality and structure of the
dynamics model, but is also capable of ignoring its inevitable inaccuracies, by
directly incorporating the evidence provided by the direct observation of the
cost. Preliminary results demonstrate that our approach outperforms purely
model-based and model-free approaches, as well as the approach of simply
switching from a model-based to a model-free setting.
