HOLA-Drone：面向零样本多无人机协同追捕的超图开放式学习
HOLA-Drone: Hypergraphic Open-ended Learning for Zero-Shot Multi-Drone Cooperative Pursuit
AI总结：
本文提出HOLA-Drone超图开放式学习算法，将零样本协调研究从双人游戏扩展到多无人机协同追捕场景，通过超图博弈建模持续调整学习目标，实验证明其在未知队友协调中优于基线方法并具备真实系统可行性。
摘要：
零样本协调（ZSC）是多智能体协作中的一个重大挑战，旨在开发能够与未曾遇到过的未知伙伴进行协调的智能体。近期前沿的ZSC方法主要聚焦于双人视频游戏，如OverCooked!2和Hanabi。在本文中，我们将ZSC研究的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知伙伴协调以捕获多个逃避者的无人机智能体。我们提出了一种新颖的超图开放式学习算法（HOLA-Drone），该算法基于我们的超图形式博弈建模持续调整学习目标，旨在提升与多个未知无人机队友的协作能力。为了实证验证HOLA-Drone的有效性，我们构建了两个不同的未知无人机队友池，以评估它们与各种未知伙伴协调的性能。实验结果表明，HOLA-Drone在与未知无人机队友协调方面优于基线方法。此外，真实世界实验验证了HOLA-Drone在物理系统中的可行性。视频可在项目主页https://sites.google.com/view/hola-drone上找到。
英文摘要：
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}.
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。
