Bridging RL and MPC for mixed-integer optimal control with application to Formula 1 race strategies

We propose a hybrid reinforcement learning (RL) and model predictive control (MPC) framework for mixed-integer optimal control, where discrete variables enter the cost and dynamics but not the constraints. Existing hierarchical approaches learn a policy only for the discrete action space, leaving co