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arXiv · 2605.22748

Superhuman Safe and Agile Racing through Multi-Agent Reinforcement Learning

Ismail Geles·Leonard Bauersfeld·Markus Wulfmeier·Davide Scaramuzza
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Abstract

Autonomous systems have achieved superhuman performance in isolation or simulation, yet they remain brittle in shared, dynamic real-world spaces. This failure stems from the dominant single-agent paradigm for physical applications, where other actors are ignored or treated as environmental noise, preventing effective coordination. Here we show that multi-agent reinforcement learning provides the essential safety scaffolding required for real-world interaction. Using high-speed quadrotor racing as a high-stakes testbed, we train agents to navigate complex aerodynamic interactions and strategic maneuvering with a variable number of racers. Through league-based self-play, agents evolve sophisticated anticipatory behaviors, including proactive collision avoidance, overtaking, and handling multi-agent physical interactions, including aerodynamic downwash. Our agents outperform a champion-level human pilot in multi-player races at speeds exceeding 22 m/s, while simultaneously reducing collision rates by 50 % compared to state-of-the-art single-agent baselines. Crucially, training with diverse artificial agents enables zero-shot generalization to safer human interaction. These results suggest that the path to robust robotic co-existence lies not in isolated safety constraints, but in the rigorous demands of multi-agent interaction. Multimedia materials are available at: https://rpg.ifi.uzh.ch/marl

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Ismail Geles, Leonard Bauersfeld, Markus Wulfmeier, Davide Scaramuzza. 2026-05-21. Superhuman Safe and Agile Racing through Multi-Agent Reinforcement Learning. https://arxiv.org/abs/2605.22748

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