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[2602.23056] Learning-based Multi-agent Race Strategies in Formula 1
Summary
This paper presents a reinforcement learning approach to optimize multi-agent race strategies in Formula 1, focusing on energy management, tire degradation, and pit-stop decisions.
Why It Matters
As Formula 1 racing evolves, leveraging AI for strategy optimization can significantly enhance performance. This research introduces a novel framework that adapts to real-time race conditions, providing valuable insights for race strategists and teams aiming for competitive advantage.
Key Takeaways
- Introduces a reinforcement learning framework for race strategy optimization.
- Agents learn to adapt strategies based on competitors' actions and race conditions.
- The approach includes an interaction module for improved decision-making.
- Results demonstrate effective pit timing, tire selection, and energy allocation.
- Framework can assist strategists in real-time decision-making during races.
Computer Science > Artificial Intelligence arXiv:2602.23056 (cs) [Submitted on 26 Feb 2026] Title:Learning-based Multi-agent Race Strategies in Formula 1 Authors:Giona Fieni, Joschua Wüthrich, Marc-Philippe Neumann, Christopher H. Onder View a PDF of the paper titled Learning-based Multi-agent Race Strategies in Formula 1, by Giona Fieni and 3 other authors View PDF HTML (experimental) Abstract:In Formula 1, race strategies are adapted according to evolving race conditions and competitors' actions. This paper proposes a reinforcement learning approach for multi-agent race strategy optimization. Agents learn to balance energy management, tire degradation, aerodynamic interaction, and pit-stop decisions. Building on a pre-trained single-agent policy, we introduce an interaction module that accounts for the behavior of competitors. The combination of the interaction module and a self-play training scheme generates competitive policies, and agents are ranked based on their relative performance. Results show that the agents adapt pit timing, tire selection, and energy allocation in response to opponents, achieving robust and consistent race performance. Because the framework relies only on information available during real races, it can support race strategists' decisions before and during races. Subjects: Artificial Intelligence (cs.AI); Systems and Control (eess.SY) Cite as: arXiv:2602.23056 [cs.AI] (or arXiv:2602.23056v1 [cs.AI] for this version) https://doi.org/10.48550...