发表状态 | 已发表Published |
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
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ISSN/eISSN | 2227-7390 |
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) |
DOI | 10.3390/math13010046 |
URL | 查看来源 |
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 | |
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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