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Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperationoa mark
  • Park, Soohyun ;
  • Kim, Jae Pyoung ;
  • Park, Chanyoung ;
  • Jung, Soyi ;
  • Kim, Joongheon
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Publication Year
2024-06-01
Journal
IEEE Communications Magazine
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
IEEE Communications Magazine, Vol.62 No.6, pp.106-112
Mesh Keyword
Autonomous mobilitiesConvergenceConvergence difficultyFast convergenceMachine learning algorithmsMobility systemsMulti-agent reinforcement learningProjection valueQuantum ComputingReinforcement learnings
All Science Classification Codes (ASJC)
Computer Science ApplicationsComputer Networks and CommunicationsElectrical and Electronic Engineering
Abstract
For Industry 4.0 Revolution, cooperative autonomous mobility systems are widely used based on multi-agent reinforcement learning (MARL). However, the MARL-based algorithms suffer from huge parameter utilization and convergence difficulties with many agents. To tackle these problems, a quantum MARL (QMARL) algorithm based on the concept of actor-critic network is proposed, which is beneficial in terms of scalability, to deal with the limitations in the noisy intermediate-scale quantum (NISQ) era. Additionally, our QMARL is also beneficial in terms of efficient parameter utilization and fast convergence due to quantum supremacy. Note that the reward in our QMARL is defined as task precision over computation time in multiple agents, thus, multi-agent cooperation can be realized. For further improvement, an additional technique for scalability is proposed, which is called projection value measure (PVM). Based on PVM, our proposed QMARL can achieve the highest reward by reducing the action dimension into a logarithmic-scale. Finally, we can conclude that our proposed QMARL with PVM outperforms the other algorithms in terms of efficient parameter utilization, fast convergence, and scalability.
ISSN
1558-1896
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/33635
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85169695433&origin=inward
DOI
https://doi.org/2-s2.0-85169695433
Journal URL
https://ieeexplore.ieee.org/servlet/opac?punumber=35
Type
Article
Funding
This research was funded by the National Research Foundation of Korea (2022R1A2C2004869).
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