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Open Access

Optimizing Risk-Aware Task Migration Algorithm Among Multiplex UAV Groups Through Hybrid Attention Multi-Agent Reinforcement Learning

School of Computer Science and Engineering, Southeast University, Nanjing 211189
School of Software Engineering, Southeast University, Nanjing 211189
School of Cyber Science and Engineering, Southeast University, Nanjing 211189
PredictHQ, Auckland 1010, New Zealand

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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.

Keywords

Unmanned Aerial Vehicle (UAV)multiplex UAV group structurestask migrationmulti-agent reinforcement learning

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Tsinghua Science and Technology
Volume 30 Issue 1,
February 2025
Pages 318-330
DOI: 10.26599/TST.2024.9010013
Cite this article:
Jiang Y, Di K, Qian R, et al. Optimizing Risk-Aware Task Migration Algorithm Among Multiplex UAV Groups Through Hybrid Attention Multi-Agent Reinforcement Learning. Tsinghua Science and Technology, 2025, 30(1): 318-330. https://doi.org/10.26599/TST.2024.9010013

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Received: 06 November 2023
Revised: 25 December 2023
Accepted: 04 January 2024
Published: 01 April 2024
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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