{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00225418","sets":["1164:1579:11081:11179"]},"path":["11179"],"owner":"44499","recid":"225418","title":["局所グラフ情報を用いた強化学習によるAGVの経路スケジューリング手法の検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-03-16"},"_buckets":{"deposit":"9b7c8c3d-58cc-4b6b-b3e1-e76755c10393"},"_deposit":{"id":"225418","pid":{"type":"depid","value":"225418","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"局所グラフ情報を用いた強化学習によるAGVの経路スケジューリング手法の検討","author_link":["596380","596382","596381","596378","596379","596377"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"局所グラフ情報を用いた強化学習によるAGVの経路スケジューリング手法の検討"},{"subitem_title":"A study of reinforcemtent learning-based AGV route scheduling using local graph information","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"深層学習","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2023-03-16","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"慶應義塾大学"},{"subitem_text_value":"慶應義塾大学"},{"subitem_text_value":"慶應義塾大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Keio University","subitem_text_language":"en"},{"subitem_text_value":"Keio University","subitem_text_language":"en"},{"subitem_text_value":"Keio University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/225418/files/IPSJ-ARC23252031.pdf","label":"IPSJ-ARC23252031.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-ARC23252031.pdf","filesize":[{"value":"2.0 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"16"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"030cc933-193d-4a43-9604-450f5657abf1","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"杉本, 寛直"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"シュレスタマリ, サソット"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"近藤, 正章"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hirotada, Sugimoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shaswot, Shresthamali","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masaaki, Kondo","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10096105","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8574","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本稿では，複数台の無人搬送車 (AGV) を実環境で使用する際に必要な経路計画を強化学習により行う手法を検討する．特に，AGV 間の行動や経路情報となる局所グラフの情報を利用し，かつ入力次元の抑制のため各 AGV の行動可能範囲に限定して抽出したノード情報を用いた強化学習によるスケジューリング手法を提案する．本手法により，全ノードの情報を利用するよりも実環境に近い問題設定においてタスク処理のスループット向上を期待できることがわかった．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In this paper, we propose a reinforcement learning-based route planning method for multiple AGVs. The proposed scheduling method uses a local graphs stricture that provide information on the actions and routes among AGVs, and also uses node information extracted only within the scope where each AGV can act in order to reduce the input dimension. We found that the proposed method can improve the throughput of task processing in a realistic problem setting compared with the case that uses entire graph information.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告システム・アーキテクチャ（ARC）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2023-03-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"31","bibliographicVolumeNumber":"2023-ARC-252"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:24:55.837435+00:00","updated":"2025-01-19T12:49:07.015333+00:00","id":225418}