# Learning-based Multi-agent Race Strategies in Formula 1

arXiv: https://arxiv.org/abs/2602.23056 · HTML: https://mlsanity.com/paper/2602.23056/

## Key facts

- Cited by 1 papers 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 (most cited first, 8 of 8)

- [Interaction-Aware Multi-Agent Reinforcement Learning for Mobile Agents with Individual Goals](https://mlsanity.com/paper/1909.12925/) (2019)
- [Towards Learning-Based Formula 1 Race Strategies](https://mlsanity.com/paper/2512.21570/) (2025)
- [Maximum-distance Race Strategies for a Fully Electric Endurance Race Car](https://mlsanity.com/paper/2111.05784/) (2021)
- [Mastering Nordschleife -- A comprehensive race simulation for AI strategy decision-making in motorsports](https://mlsanity.com/paper/2306.16088/) (2023)
- [Model Predictive Control Strategies for Electric Endurance Race Cars Accounting for Competitors Interactions](https://mlsanity.com/paper/2403.06885/) (2024)
- [Competitors-Aware Stochastic Lap Strategy Optimisation for Race Hybrid Vehicles](https://mlsanity.com/paper/2203.00084/) (2022)
- [Explainable Reinforcement Learning for Formula One Race Strategy](https://mlsanity.com/paper/2501.04068/) (2025)
- [Game Theory in Formula 1: From Physical to Strategic Interactions](https://mlsanity.com/paper/2503.05421/) (2025)

## Built on by (most cited first, 1 of 1)

- [Competitor-aware Race Management for Electric Endurance Racing](https://mlsanity.com/paper/2603.28286/) (2026)

## 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.