HOLA-Drone: Hypergraphic Open-ended Learning for Zero-Shot Multi-Drone Cooperative Pursuit
<|reference_start|>HOLA-Drone: Hypergraphic Open-ended Learning for Zero-Shot Multi-Drone Cooperative Pursuit: 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}.<|reference_end|>
@article{li2024hola-drone:,
    title={HOLA-Drone: Hypergraphic Open-ended Learning for Zero-Shot Multi-Drone
  Cooperative Pursuit},
    author={Yang Li, Dengyu Zhang, Junfan Chen, Ying Wen, Qingrui Zhang, Shaoshuai
  Mou, Wei Pan},
    journal={arXiv preprint arXiv:2409.08767},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08767},
    primaryClass={cs.RO cs.AI}
}