Abstraction-based branch and bound approach to Q-learning for hybrid optimal control
Proceedings of the 3rd Conference on Learning for Dynamics and Control, PMLR 144:263-274, 2021.
In this paper, we design a theoretical framework allowing to apply model predictive control on hybrid systems. For this, we develop a theory of approximate dynamic programming by leveraging the concept of alternating simulation. We show how to combine these notions in a branch and bound algorithm that can further refine the Q-functions using Lagrangian duality. We illustrate the approach on a numerical example.