Multi-UAVs Strategies for Ad Hoc Network with Multi-Agent Reinforcement Learning

被引：1

作者：

Karimata, George ^{[1
]}

Nakazato, Jin ^{[1
]}

Tran, Gia Khanh ^{[2
]}

Suto, Katsuya ^{[3
]}

Tsukada, Manabu ^{[1
]}

Esaki, Hiroshi ^{[1
]}

机构：

[1] Univ Tokyo, Tokyo, Japan

[2] Tokyo Inst Technol, Tokyo, Japan

[3] Univ Electrocommun, Chofu, Tokyo, Japan

来源：

38TH INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING, ICOIN 2024 | 2024年

关键词：

UAV; flying ad hoc network; multi-agent reinforcement learning; multi-agent transformer;

D O I：

10.1109/ICOIN59985.2024.10572031

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In recent years, extensive research has focused on leveraging advanced technologies beyond 5G and for Industry 5.0 to promote sustainability and prosperity in society. Our study advances this effort by seeking to create an aerial perspective using Unmanned Aerial Vehicles (UAVs). This paper introduces a method for optimizing UAV deployment strategies using multiagent reinforcement learning, facilitating the formation of a flying ad hoc network. The results demonstrate practical cooperation among UAVs in flight.

引用

页码：726 / 729

页数：4

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