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题名Deep Reinforcement Learning Algorithm with Long Short-Term Memory Network for Optimizing Unmanned Aerial Vehicle Information Transmission
作者
发表日期2025
发表期刊Mathematics
ISSN/eISSN2227-7390
卷号13期号:1
摘要

The optimization of information transmission in unmanned aerial vehicles (UAVs) is essential for enhancing their operational efficiency across various applications. This issue is framed as a mixed-integer nonconvex optimization challenge, which traditional optimization algorithms and reinforcement learning (RL) methods often struggle to address effectively. In this paper, we propose a novel deep reinforcement learning algorithm that utilizes a hybrid discrete–continuous action space. To address the long-term dependency issues inherent in UAV operations, we incorporate a long short-term memory (LSTM) network. Our approach accounts for the specific flight constraints of fixed-wing UAVs and employs a continuous policy network to facilitate real-time flight path planning. A non-sparse reward function is designed to maximize data collection from internet of things (IoT) devices, thus guiding the UAV to optimize its operational efficiency. Experimental results demonstrate that the proposed algorithm yields near-optimal flight paths and significantly improves data collection capabilities, compared to conventional heuristic methods, achieving an improvement of up to 10.76%. Validation through simulations confirms the effectiveness and practicality of the proposed approach in real-world scenarios.

关键词deep reinforcement learning (DRL) long short-term memory (LSTM) nonconvex optimization optimal control unmanned aerial vehicle (UAV)
DOI10.3390/math13010046
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收录类别SCIE
语种英语English
WOS研究方向Mathematics
WOS类目Mathematics
WOS记录号WOS:001393624100001
Scopus入藏号2-s2.0-85214506518
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/12542
专题北师香港浸会大学
通讯作者Liang, Kewei
作者单位
1.Polytechnic Institute,Zhejiang University,Hangzhou,310015,China
2.Department of Applied Mathematics,Hong Kong Polytechnic University,Hong Kong
3.School of Mathematical Sciences,Zhejiang University,Hangzhou,310058,China
4.Applied Mathematics,Beijing Normal University—Hong Kong Baptist University United International College,Zhuhai,519087,China
推荐引用方式
GB/T 7714
He, Yufei,Hu, Ruiqi,Liang, Keweiet al. Deep Reinforcement Learning Algorithm with Long Short-Term Memory Network for Optimizing Unmanned Aerial Vehicle Information Transmission[J]. Mathematics, 2025, 13(1).
APA He, Yufei, Hu, Ruiqi, Liang, Kewei, Liu, Yonghong, & Zhou, Zhiyuan. (2025). Deep Reinforcement Learning Algorithm with Long Short-Term Memory Network for Optimizing Unmanned Aerial Vehicle Information Transmission. Mathematics, 13(1).
MLA He, Yufei,et al."Deep Reinforcement Learning Algorithm with Long Short-Term Memory Network for Optimizing Unmanned Aerial Vehicle Information Transmission". Mathematics 13.1(2025).
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