RL is a very successful approach to optimal control, which, however, struggles to provide explainability and strong guarantees on the behavior of the resulting control scheme. In contrast, MPC is a standard tool for the closed-loop optimal control of complex systems with constraints and limitations and benefits from a rich theory to assess closed-loop behavior. Because of model inaccuracy, however, MPC can fail at delivering satisfactory closed-loop performance. This seminar will discuss how to leverage the advantages of the two techniques, offering a path toward safe and explainable RL.

Mario Zanon received the Master's degree in Mechatronics from the University of Trento, and the Diplôme d'Ingénieur from the Ecole Centrale Paris, in 2010. After research stays at the KU Leuven, University of Bayreuth, Chalmers University, and the University of Freiburg he received the Ph.D. degree in Electrical Engineering from the KU Leuven in November 2015. He held a Post-Doc researcher position at Chalmers University until the end of 2017 and is now Associate Professor at the IMT School for Advanced Studies Lucca. His research interests include optimal control and estimation of nonlinear dynamic systems, in particular for aerospace and automotive applications, economic MPC, numerical methods for optimization, and reinforcement learning.