# Competitor-aware Race Management for Electric Endurance Racing

arXiv: https://arxiv.org/abs/2603.28286 · HTML: https://mlsanity.com/paper/2603.28286/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 30 Mar 2026.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 8 of 8)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Emergent Complexity via Multi-Agent Competition](https://mlsanity.com/paper/1710.03748/) (2017)
- [Double Oracle Algorithm for Computing Equilibria in Continuous Games](https://mlsanity.com/paper/2009.12185/) (2020)
- [Towards Learning-Based Formula 1 Race Strategies](https://mlsanity.com/paper/2512.21570/) (2025)
- [Solving Zero-Sum Games through Alternating Projections](https://mlsanity.com/paper/2010.00109/) (2020)
- [Maximum-distance Race Strategies for a Fully Electric Endurance Race Car](https://mlsanity.com/paper/2111.05784/) (2021)
- [Model Predictive Control Strategies for Electric Endurance Race Cars Accounting for Competitors Interactions](https://mlsanity.com/paper/2403.06885/) (2024)
- [Learning-based Multi-agent Race Strategies in Formula 1](https://mlsanity.com/paper/2602.23056/) (2026)

## Abstract (arXiv)

Electric endurance racing is characterized by severe energy constraints and strong aerodynamic interactions. Determining race-winning policies therefore becomes a fundamentally multi-agent, game-theoretic problem. These policies must jointly govern low-level driver inputs as well as high-level strategic decisions, including energy management and charging. This paper proposes a bi-level framework for competitor-aware race management that combines game-theoretic optimal control with reinforcement learning. At the lower level, a multi-agent game-theoretic optimal control problem is solved to capture aerodynamic effects and asymmetric collision-avoidance constraints inspired by motorsport rules. Using this single-lap problem as the environment, reinforcement learning agents are trained to allocate battery energy and schedule pit stops over an entire race. The framework is demonstrated in a two-agent, 45-lap simulated race. The results show that effective exploitation of aerodynamic interactions is decisive for race outcome, with strategies that prioritize finishing position differing fundamentally from single-agent, minimum-time approaches.