@inproceedings{3eb5907092f949bf80c029d52d5f0e36,
title = "Multi-UAV Cooperative Multi-objective Task Allocation Based on Deep Reinforcement Learning",
abstract = "This paper proposes a task allocation algorithm based on the combination of reinforcement learning and deep neural networks to address the problem of multi-UAV cooperative multi-objective task allocation. It utilizes graph neural networks (GNN) and attention mechanisms to model the policy function, thereby constructing a task allocation strategy based on the collective state of the UAV swarm which enables the reinforcement learning algorithm to generalize to varying numbers of enemy target nodes in the environment. To improve the efficiency and stability of training, the S-sample batch reinforcement learning algorithm is adopted. The simulation results demonstrate that the algorithm can effectively solve the multi-UAV task allocation problem.",
keywords = "Attention Mechanisms, Graph Neural Networks, Multi-UAV, Reinforcement Learning, Task Allocation",
author = "Jingyi Guo and Shunmin Li and Guanqun Wu and Aijun Li and Yong Guo",
note = "Publisher Copyright: {\textcopyright} 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Guidance, Navigation and Control, ICGNC 2024 ; Conference date: 09-08-2024 Through 11-08-2024",
year = "2025",
doi = "10.1007/978-981-96-2244-3_33",
language = "英语",
isbn = "9789819622436",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "341--352",
editor = "Liang Yan and Haibin Duan and Yimin Deng",
booktitle = "Advances in Guidance, Navigation and Control - Proceedings of 2024 International Conference on Guidance, Navigation and Control Volume 12",
}