  Reinforcement learning methods typically use Deep Neural Networks to
approximate the value functions and policies underlying a Markov Decision
Process. Unfortunately, DNN-based RL suffers from a lack of explainability of
the resulting policy. In this paper, we instead approximate the policy and
value functions using an optimization problem, taking the form of Quadratic
Programs (QPs). We propose simple tools to promote structures in the QP,
pushing it to resemble a linear MPC scheme. A generic unstructured QP offers
high flexibility for learning, while a QP having the structure of an MPC scheme
promotes the explainability of the resulting policy, additionally provides ways
for its analysis. The tools we propose allow for continuously adjusting the
trade-off between the former and the latter during learning. We illustrate the
workings of our proposed method with the resulting structure using a point-mass
task.
