HOLA-Drone: ハイパーグラフ型オープンエンド学習によるゼロショット複数ドローン協調追跡
HOLA-Drone: Hypergraphic Open-ended Learning for Zero-Shot Multi-Drone Cooperative Pursuit
ハイパーグラフ形式のゲームモデリングに基づき学習目標を継続的に適応させる手法を提案し、未見の複数ドローン仲間と協調して複数の逃走者を捕獲するゼロショット協調を実現した。
著者: Yang Li, Dengyu Zhang, Junfan Chen, Ying Wen, Qingrui Zhang, Shaoshuai Mou, Wei Pan
分類: cs.RO, cs.AI
原文アブストラクト
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}.
