Poster Session 2, 11:30 AM - 12:15 PM: Room 163 [C4]

Presenter: Shayna Mullett

Faculty Sponsor: Ali Al-Faris

School: Worcester State University

Research Area: Computer Science

ABSTRACT

This project presents a Python-based AI race strategy assistant developed for Formula 1, designed to optimize race outcomes for a single car–driver combination. The system employs reinforcement learning to model strategic decision-making across race conditions, using historical data from the 2022–2024 seasons as its training environment. By learning through iterative reward-based optimisation—where finishing position and race time serve as key performance signals—the agent develops strategies for tyre selection, pit stop timing, and stint management under varying competitive scenarios.

The reinforcement learning framework enables the agent to evaluate long-term trade-offs, such as track position versus tyre degradation, and to adapt its policy based on evolving race contexts. The model is trained on historical race states and outcomes, allowing it to approximate realistic race evolution while learning optimal responses to both predictable and stochastic events.

A central feature of the system is its dynamic adaptability. During simulation, users can manually introduce race events such as safety cars, punctures, lock-ups, or time penalties. These interventions modify the race state in real time, prompting the agent to recompute and adjust its strategy to maximise finishing position under updated constraints.

By combining historical data-driven modelling with reinforcement learning and interactive adaptability, this project demonstrates the potential of AI-assisted strategic decision systems in high-performance motorsport and other dynamic, uncertainty-rich environments.