Optimal control and reinforcement learning for formula one lap simulation - Hoeppke, C (2022)
Lap simulation in a Formula One context is a subclass of optimal control problems and describes the computation of optimal trajectories around racing circuits. The results of lap simulation are primarily used for vehicle setup and strategic racing decisions. The optimal lap problem is solved using two classes of algorithms. The first algorithm uses direct collocation to compute optimal trajectories and the second algorithm uses specially constructed reinforcement learning environments and generalised function approximation to compute desirable system inputs. Historically direct collocation methods were considered impractical for minimum lap time simulations, due to their high computational costs. The exponential increase in computational performance has enabled the practical application of these algorithms.