Online Planning for F1 Race Strategy Identification


Diego Piccinotti, Amarildo Likmeta, Nicolò Brunello, Marcello Restelli


Formula 1 (F1) racing is one of the most competitive racing competitions involving high-performance single-seater racing vehicles. The result of a race is determined by vehicle and driver performance, as well as by the tire and pit-stop strategy employed in the race. In this work, we consider the problem of deciding when to pit-stop and which compound to use as a sequential decision-making problem and we investigate the application of online planning algorithms to tackle it. The availability of high-accuracy race and vehicle simulators presents a perfect opportunity to apply planning algorithms, which require a model of the environment to search for the best policies to apply. To this end, we investigate the feasibility of applying online planning to the specific problem of race-strategy identification and propose an open-loop approach that combines Monte Carlo sampling and Temporal Difference (TD) updates to identify whether to perform a pitstop at each race lap and which tire compound to employ. Furthermore, we perform an evaluation of different planning algorithms using a simulator based on (Heilmeier et al. 2020a), which we modify to be consistent with a planning application.


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