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DDPG-based Deep Reinforcement Learning for Loitering Munition Mobility Control: Algorithm Design and Visualization
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Publication Year
2022-01-01
Journal
APWCS 2022 - 2022 IEEE VTS Asia Pacific Wireless Communications Symposium
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
APWCS 2022 - 2022 IEEE VTS Asia Pacific Wireless Communications Symposium, pp.112-116
Keyword
DDPGDroneLoitering munitionReinforcement learningUnity
Mesh Keyword
'currentAlgorithm designAlgorithm visualizationDDPGLoitering munitionMobility controlModern warfareReinforcement learningsUkraineUnity
All Science Classification Codes (ASJC)
Safety, Risk, Reliability and QualityArtificial IntelligenceComputer Networks and CommunicationsSignal Processing
Abstract
Drone technology is estimated for its potential to be applied in many industries, including logistics, broadcasting, telecommunications, and warfare technology. In particular, in the field of modern warfare such as the current war in Ukraine, the use of drones has become an essential element. This paper includes a loitering munition to attack a single ground target in the scenario. A simulation environment for drone attack is built based on the 3D platform Unity, and learning is performed by applying DDPG, a reinforcement learning algorithm that can be used in continuous action space. Through the specific result, it is possible to achieve our purpose to attack target exactly.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36781
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85141443635&origin=inward
DOI
https://doi.org/10.1109/apwcs55727.2022.9906493
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9906453
Type
Conference
Funding
ACKNOWLEDGMENT This work was supported by the National Research Foundation of Korea (2021R1A4A1030775) and also by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2022-2017-0-01637) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation). Soyi Jung, Jae-Hyun Kim, and Joongheon Kim are the corresponding authors of this paper.
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Jung, Soyi Image
Jung, Soyi정소이
Department of Electrical and Computer Engineering
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