Benchmarking Learning-Based Formula 1 Race Strategies
The research developed two methods to optimize Formula 1 race strategies by simultaneously managing energy allocation, tire wear, and pit stop timing. A reinforcement learning agent was trained to find a near-optimal pit stop strategy quickly, outperforming a complex mathematical model. This demonstrates that RL can provide robust, real-time decision-making for racing challenges.