@article{Jiang2025, 
author = {Yuanshuang Jiang and Kai Di and Ruiyi Qian and Xingyu Wu and Fulin Chen and Pan Li and Xiping Fu and Yichuan Jiang},
title = {Optimizing Risk-Aware Task Migration Algorithm Among Multiplex UAV Groups Through Hybrid Attention Multi-Agent Reinforcement Learning},
year = {2025},
journal = {Tsinghua Science and Technology},
volume = {30},
number = {1},
pages = {318-330},
keywords = {Unmanned Aerial Vehicle (UAV), task migration, multi-agent reinforcement learning, multiplex UAV group structures},
url = {https://www.sciopen.com/article/10.26599/TST.2024.9010013},
doi = {10.26599/TST.2024.9010013},
abstract = {Recently, with the increasing complexity of multiplex Unmanned Aerial Vehicles (multi-UAVs) collaboration in dynamic task environments, multi-UAVs systems have shown new characteristics of inter-coupling among multiplex groups and intra-correlation within groups. However, previous studies often overlooked the structural impact of dynamic risks on agents among multiplex UAV groups, which is a critical issue for modern multi-UAVs communication to address. To address this problem, we integrate the influence of dynamic risks on agents among multiplex UAV group structures into a multi-UAVs task migration problem and formulate it as a partially observable Markov game. We then propose a Hybrid Attention Multi-agent Reinforcement Learning (HAMRL) algorithm, which uses attention structures to learn the dynamic characteristics of the task environment, and it integrates hybrid attention mechanisms to establish efficient intra- and inter-group communication aggregation for information extraction and group collaboration. Experimental results show that in this comprehensive and challenging model, our algorithm significantly outperforms state-of-the-art algorithms in terms of convergence speed and algorithm performance due to the rational design of communication mechanisms.}
}