Bridging RL and MPC for mixed-integer optimal control with application to Formula 1 race strategies
This work addresses optimal control with discrete variables for applications like autonomous racing, offering a modular and adaptable solution, though it is incremental as it builds on existing RL and MPC methods.
The authors tackled the problem of mixed-integer optimal control by proposing a hybrid RL-MPC framework that trains an RL agent on the full hybrid action space and integrates it with MPC for deployment, achieving near-optimal performance relative to an offline benchmark and outperforming standalone RL in a Formula 1 race strategy application.
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 use RL only for the discrete action space, leaving continuous optimization to MPC. Unlike these methods, we train the RL agent on the full hybrid action space, ensuring consistency with the cost of the underlying Markov decision process. During deployment, the RL actor is rolled out over the prediction horizon to parametrize an integer-free nonlinear MPC through the discrete action sequence and provide a continuous warm-start. The learned critic serves as a terminal cost to capture long-term performance. We prove recursive feasibility, and validate the framework on a Formula 1 race strategy problem. The hybrid method achieves near-optimal performance relative to an offline mixed-integer nonlinear program benchmark, outperforming a standalone RL agent. Moreover, the hybrid scheme enables adaptation to unseen disturbances through modular MPC extensions at zero retraining cost.