Superhuman Safe and Agile Racing through Multi-Agent Reinforcement Learning
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 sophi...
| Authors | Ismail Geles, Leonard Bauersfeld, Markus Wulfmeier et al. |
|---|---|
| Primary category | cs.RO |
| Published | 21 May 2026 |
| Last updated at source | 17 June 2026 |
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