  Reinforcement Learning (RL) has demonstrated a huge potential in learning
optimal policies without any prior knowledge of the process to be controlled.
Model Predictive Control (MPC) is a popular control technique which is able to
deal with nonlinear dynamics and state and input constraints. The main drawback
of MPC is the need of identifying an accurate model, which in many cases cannot
be easily obtained. Because of model inaccuracy, MPC can fail at delivering
satisfactory closed-loop performance. Using RL to tune the MPC formulation or,
conversely, using MPC as a function approximator in RL allows one to combine
the advantages of the two techniques. This approach has important advantages,
but it requires an adaptation of the existing algorithms. We therefore propose
an improved RL algorithm for MPC and test it in simulations on a rather
challenging example.
