mlsanityData to 14 Oct 2026

Learning-based Multi-agent Race Strategies in Formula 1

arXiv 2602.23056 ↗First posted 26 Feb 2026cs.AI cs.SY eess.SYcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 23 references that are in the map. First posted on arXiv 26 Feb 2026.

Builds on

8 of 23 references.

References · 8

  1. 2019Interaction-Aware Multi-Agent Reinforcement Learning for Mobile Agents with Individual Goalscited by 3
  2. 2025Towards Learning-Based Formula 1 Race Strategiescited by 3
  3. 2021Maximum-distance Race Strategies for a Fully Electric Endurance Race Carcited by 2
  4. 2023Mastering Nordschleife -- A comprehensive race simulation for AI strategy decision-making in motorsportscited by 2
  5. 2024Model Predictive Control Strategies for Electric Endurance Race Cars Accounting for Competitors Interactionscited by 2
  6. 2022Competitors-Aware Stochastic Lap Strategy Optimisation for Race Hybrid Vehiclescited by 1
  7. 2025Explainable Reinforcement Learning for Formula One Race Strategycited by 1
  8. 2025Game Theory in Formula 1: From Physical to Strategic Interactionscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Competitor-aware Race Management for Electric Endurance Racingcited by 0

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  1. 2025Agile Flight Emerges from Multi-Agent Competitive Racing
  2. 2025A Champion-level Vision-based Reinforcement Learning Agent for Competitive Racing in Gran Turismo 7
  3. 2026Superhuman Safe and Agile Racing through Multi-Agent Reinforcement Learning
  4. 2024F1tenth Autonomous Racing With Offline Reinforcement Learning Methods
  5. 2025Accelerating Real-World Overtaking in F1TENTH Racing Employing Reinforcement Learning Methods
  6. 2024A Super-human Vision-based Reinforcement Learning Agent for Autonomous Racing in Gran Turismo
  7. 2021Autonomous Racing using a Hybrid Imitation-Reinforcement Learning Architecture
  8. 2026Bridging RL and MPC for mixed-integer optimal control with application to Formula 1 race strategies