| Item type |
会議発表論文 / Conference Paper(1) |
| 公開日 |
2023-03-02 |
| タイトル |
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タイトル |
マルチカーエレベータ群管理における強化学習を用いたルール選択 |
| タイトル |
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タイトル |
Rule Selection for Reinforcement Learning in Multi-Car Elevator Group Control |
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言語 |
en |
| 言語 |
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言語 |
jpn |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_5794 |
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資源タイプ |
conference paper |
| 著者 |
浦地, 太一
髙橋, 健一
URAJI, Taichi
TAKAHASHI, Kenichi
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
In this paper, we propose an efficient control method for multicar-elevator using reinforcement learning．In the method, the control agent selects the best policy among three policies, namely Distance-policy, MinGuests-policy, and Zone-policy, according to traffic flow. The control agent takes the number of total passengers and the distance from the starting floor to the destination floor of a call into account. Through experiments, we compare the mean completion time of the proposed method with the mean completion times for the cases when the car assignment is made by each of the three policies. |
| 内容記述 |
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内容記述タイプ |
Other |
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内容記述 |
2011 IEEE SMC Hiroshima Chapter若手研究会, 2011年7月9日, 広島市立大学 |
| 書誌情報 |
2011 IEEE SMC Hiroshima Chapter Young Researchers' Workshop Proceedings = 2011 IEEE SMC Hiroshima Chapter 若手研究会講演論文集
p. 41-44,
発行日 2011-07
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| 出版者 |
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出版者 |
IEEE SMC Hiroshima Chapter |
| 権利 |
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権利情報 |
Copyright by IEEE SMC Hiroshima Chapter. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting or republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| 関連サイト |
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識別子タイプ |
URI |
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関連識別子 |
http://www.smc-hiroshima.info.hiroshima-cu.ac.jp/ |
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関連名称 |
http://www.smc-hiroshima.info.hiroshima-cu.ac.jp/ |
| フォーマット |
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内容記述タイプ |
Other |
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内容記述 |
application/pdf |
| 著者版フラグ |
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出版タイプ |
AM |
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出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
SMCHiroshima2011_41.pdf (551.2 kB)
