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Integrate multi-agent simulation environment and multi-agent reinforcement learning (MARL) for real-world scenario
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Publication Year
2020-10-21
Journal
International Conference on ICT Convergence
Publisher
IEEE Computer Society
Citation
International Conference on ICT Convergence, Vol.2020-October, pp.523-525
Keyword
deep reinforcement learningMARLmulti agent simulation
Mesh Keyword
Intuitive interfacesMulti agentMulti agent simulationMulti-agent reinforcement learningMultiple agentsReal-world scenario
All Science Classification Codes (ASJC)
Information SystemsComputer Networks and Communications
Abstract
Multi-agent deep reinforcement learning has made a great achievement in deep reinforcement learning through modeling a real-world scenario with multiple agents that communicate with a single environment. However, the test and validation of MARL model on the conventional multi-agent simulation are limited. In this study, we analyze an effective method to use a multi-agent simulation to test and validate multi-agent reinforcement learning models and methods as well as propose two requirements, an intuitive interface and the optimization of simulation, to achieve it.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36590
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85098935285&origin=inward
DOI
https://doi.org/10.1109/ictc49870.2020.9289369
Journal URL
http://ieeexplore.ieee.org/xpl/conferences.jsp
Type
Conference
Funding
This research was supported by the Future Combat System Network Technology Research Center program of Defense Acquisition Program Administration and Agency for Defense Development. (UD190033ED).
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Oh, Sangyoon Image
Oh, Sangyoon오상윤
Department of Software and Computer Engineering
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