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Quantum Multi - Agent Reinforcement Learning Software Design and Visual Simulations for Multi - Drone Mobility Control
  • Park, Soohyun ;
  • Kim, Gyu Seon ;
  • Jung, Soyi ;
  • Kim, Joongheon
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Publication Year
2024-01-01
Journal
2024 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2024
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
2024 IEEE VTS Asia Pacific Wireless Communications Symposium, APWCS 2024
Keyword
DroneQuantum Machine LearningReinforce-ment LearningSimulationsVisualization
Mesh Keyword
Design simulationsLearning softwareMachine-learningMobility controlMulti-agent reinforcement learningQuantum machine learningQuantum machinesReinforce-ment learningSimulationVisual simulation
All Science Classification Codes (ASJC)
Computer Networks and CommunicationsSafety, Risk, Reliability and QualityControl and OptimizationInstrumentation
Abstract
Quantum machine learning (QML) has received a lot of attention according to its light training parameter numbers and speeds; and the advances of QML lead to active research on quantum multi-agent reinforcement learning (QMARL). Existing classical multi-agent reinforcement learning (MARL) features non-stationarity and uncertain properties. Therefore, this paper presents a simulation software framework for novel QMARL to control autonomous multi-drone mobility, i.e., quantum multi-drone reinforcement learning. Our proposed framework ac-complishes reasonable reward convergence and service quality performance with fewer trainable parameters. Furthermore, it shows more stable training results. Lastly, our proposed software allows us to analyze the training process and results.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/37119
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85206079106&origin=inward
DOI
https://doi.org/10.1109/apwcs61586.2024.10679327
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=10679110
Type
Conference
Funding
This research was funded by National Research Foundation of Korea (NRF) (2022R1A2C2004869) and also by NRF grant funded by the Korea government (MSIT) (2022R1C1C1010766).
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Jung, Soyi정소이
Department of Electrical and Computer Engineering
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