Zero-shot coordination (ZSC) is a significant challenge in multi-agent collaboration, aiming to develop agents that can coordinate with unseen partners they have not encountered before. Recent cutting-edge ZSC methods have primarily focused on two-player video games such as OverCooked!2 and Hanabi. In this paper, we extend the scope of ZSC research to the multi-drone cooperative pursuit scenario, exploring how to construct a drone agent capable of coordinating with multiple unseen partners to capture multiple evaders. We propose a novel Hypergraphic Open-ended Learning Algorithm (HOLA-Drone) that continuously adapts the learning objective based on our hypergraphic-form game modeling, aiming to improve cooperative abilities with multiple unknown drone teammates. To empirically verify the effectiveness of HOLA-Drone, we build two different unseen drone teammate pools to evaluate their performance in coordination with various unseen partners. The experimental results demonstrate that HOLA-Drone outperforms the baseline methods in coordination with unseen drone teammates. Furthermore, real-world experiments validate the feasibility of HOLA-Drone in physical systems. Videos can be found on the project homepage~\url{https://sites.google.com/view/hola-drone}.

翻译：零样本协调是多智能体协作领域的一项重大挑战，其目标是开发能够与未曾见过的伙伴进行协调的智能体。近期最先进的零样本协调方法主要集中于《OverCooked!2》和《Hanabi》等双人视频游戏。本文将零样本协调的研究范围扩展到多无人机协同追捕场景，探索如何构建一个能够与多个未见伙伴协调合作以捕获多个逃逸者的无人机智能体。我们提出了一种新颖的超图开放学习算法，该算法基于我们建立的超图形式博弈模型持续调整学习目标，旨在提升与多个未知无人机队友的协作能力。为实证验证HOLA-Drone的有效性，我们构建了两个不同的未见无人机队友池，以评估其与各类未见伙伴的协调性能。实验结果表明，在与未见无人机队友协调时，HOLA-Drone的性能优于基线方法。此外，真实世界实验验证了HOLA-Drone在物理系统中的可行性。相关视频可在项目主页~\url{https://sites.google.com/view/hola-drone}查看。