  Reinforcement Learning (RL) has recently impressed the world with stunning
results in various applications. While the potential of RL is now
well-established, many critical aspects still need to be tackled, including
safety and stability issues. These issues, while partially neglected by the RL
community, are central to the control community which has been widely
investigating them. Model Predictive Control (MPC) is one of the most
successful control techniques because, among others, of its ability to provide
such guarantees even for uncertain constrained systems. Since MPC is an
optimization-based technique, optimality has also often been claimed.
Unfortunately, the performance of MPC is highly dependent on the accuracy of
the model used for predictions. In this paper, we propose to combine RL and MPC
in order to exploit the advantages of both and, therefore, obtain a controller
which is optimal and safe. We illustrate the results with a numerical example
in simulations.
