通过多智能体强化学习实现超人类安全且敏捷的赛车
Superhuman Safe and Agile Racing through Multi-Agent Reinforcement Learning
AI总结：
本文提出通过多智能体强化学习在高速四旋翼赛车中实现安全且敏捷的性能，展示了多智能体交互对真实世界交互安全性的关键作用，同时在高速赛车中超越人类飞行员并减少碰撞率。
摘要：
自主系统在孤立或模拟环境中已实现超人类性能，但在共享、动态的真实世界空间中仍显得脆弱。这种失败源于物理应用中主导的单智能体范式，其中其他参与者被忽略或视为环境噪声，阻碍了有效协调。本文证明多智能体强化学习为真实世界交互提供了必要的安全性基础。使用高速四旋翼赛车作为高风险测试平台，训练智能体在复杂空气动力学相互作用和战略机动中导航，具有可变数量的赛车。通过联赛基于的自我对战，智能体进化出复杂的前瞻性行为，包括主动避障、超车和处理多智能体物理交互，包括空气动力学下洗。我们的智能体在超过22米/秒的速度下多玩家赛车中超越了冠军级人类飞行员，同时与最先进的单智能体基线相比，碰撞率减少了50%。关键的是，使用多样化的人工智能体进行训练能够实现零样本泛化到更安全的人类交互。这些结果表明，实现稳健的机器人共存的路径不在于孤立的安全约束，而在于多智能体交互的严格要求。多媒体材料可在：https://rpg.ifi.uzh.ch/marl
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
