mlsanityData to 14 Oct 2026

Competitor-aware Race Management for Electric Endurance Racing

arXiv 2603.28286 ↗First posted 30 Mar 2026eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

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.

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.

Builds on

8 of 35 references.

References · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Emergent Complexity via Multi-Agent Competitioncited by 120
  3. 2020Double Oracle Algorithm for Computing Equilibria in Continuous Gamescited by 9
  4. 2025Towards Learning-Based Formula 1 Race Strategiescited by 3
  5. 2020Solving Zero-Sum Games through Alternating Projectionscited by 2
  6. 2021Maximum-distance Race Strategies for a Fully Electric Endurance Race Carcited by 2
  7. 2024Model Predictive Control Strategies for Electric Endurance Race Cars Accounting for Competitors Interactionscited by 2
  8. 2026Learning-based Multi-agent Race Strategies in Formula 1cited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Superhuman Safe and Agile Racing through Multi-Agent Reinforcement Learning
  2. 2025Agile Flight Emerges from Multi-Agent Competitive Racing
  3. 2026Deep Reinforcement-Learning-Guided Model Predictive Control for Preventing Overtakes in Autonomous Racing
  4. 2025Explainable Reinforcement Learning for Formula One Race Strategy
  5. 2024Trajectory Planning Using Reinforcement Learning for Interactive Overtaking Maneuvers in Autonomous Racing Scenarios
  6. 2025On learning racing policies with reinforcement learning
  7. 2026Expert Knowledge-driven Reinforcement Learning for Autonomous Racing via Trajectory Guidance and Dynamics Constraints
  8. 2024F1tenth Autonomous Racing With Offline Reinforcement Learning Methods