Zobrazeno 1 - 10 of 40 pro vyhledávání: '"Masato, NAGAYOSHI"'
2
Factors Affecting School Support for Students in Poverty
Autor: Moeka, NOMURA, Hiromi, TODOME, Masato, NAGAYOSHI
Publikováno v: 上越教育大学教職大学院研究紀要 = Bulletin of Teaching Profession Graduate School Joetsu University of Education. 10:185-194
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=jairo_______::db6ed4170f47a6dab0281e5c71a04b28
http://hdl.handle.net/10513/00008545
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6
A Promoting Method of Role Differentiation using a Learning Rate that has a Periodically Negative Value in Multi-agent Reinforcement Learning
Autor: Masato Nagayoshi, Simon J. H. Elderton, Hisashi Tamaki
Publikováno v: Journal of Robotics, Networking and Artificial Life (JRNAL), Vol 6, Iss 4 (2020)
There have been many studies on Multi-Agent Reinforcement Learning (MARL) in which each autonomous agent obtains its own control rule by Reinforcement Learning (RL). Here, we hypothesize that different agents having individuality is more effective th
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bdf1ea336a5f9f07731e9b6b4b2221f8
https://www.atlantis-press.com/article/125935234/view
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7
Reinforcement Learning Approach for Adaptive Negotiation-Rules Acquisition in AGV Transportation Systems
Autor: Masato Nagayoshi, Kazutoshi Sakakibara, Hisashi Tamaki, Simon J. H. Elderton
Publikováno v: Journal of Advanced Computational Intelligence and Intelligent Informatics. 21:948-957
In this paper, we introduce an autonomous decentralized method for directing multiple automated guided vehicles (AGVs) in response to uncertain delivery requests. The transportation route plans of AGVs are expected to minimize the transportation time
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d6f168e3018a3a6f22acac45d79744d9
https://doi.org/10.20965/jaciii.2017.p0948
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10
A Multi-agent Reinforcement Learning Method for Role Differentiation Using State Space Filters with Fluctuation Parameters
Autor: Masato Nagayoshi, Hisashi Tamaki, Simon J. H. Elderton
Publikováno v: Journal of Robotics, Networking and Artificial Life (JRNAL), Vol 8, Iss 1 (2021)
Recently, there have been many studies on Multi-agent Reinforcement Learning (MARL) in which each autonomous agent obtains its own control rule by RL. Here, we hypothesize that different agents having individuality is more effective than uniform agen
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d82cf14a2dbd87375ddfd02a37f3eebe
https://doi.org/10.2991/jrnal.k.210521.002
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