{"updated":"2025-01-20T04:40:17.970928+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00180877","sets":["6504:9168:9182"]},"path":["9182"],"owner":"6748","recid":"180877","title":["転移学習によるDeep Q-Networkの学習高速化に向けた検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-03-16"},"_buckets":{"deposit":"d13cb476-a005-4277-9b65-194695438c9b"},"_deposit":{"id":"180877","pid":{"type":"depid","value":"180877","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"転移学習によるDeep Q-Networkの学習高速化に向けた検討","author_link":["391140","391139","391141","391143","391142"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"転移学習によるDeep Q-Networkの学習高速化に向けた検討"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2017-03-16","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"横浜国大"},{"subitem_text_value":"横浜国大"},{"subitem_text_value":"横浜国大"},{"subitem_text_value":"横浜国大"},{"subitem_text_value":"横浜国大"}]},"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/180877/files/IPSJ-Z79-5M-09.pdf","label":"IPSJ-Z79-5M-09.pdf"},"date":[{"dateType":"Available","dateValue":"2017-05-22"}],"format":"application/pdf","filename":"IPSJ-Z79-5M-09.pdf","filesize":[{"value":"445.9 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"d047e305-9afd-40eb-9c23-54abafcf2cb8","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2017 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"足立, 一樹"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"佐々木, 勇人"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中田, 雅也"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"濱津, 文哉"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"濱上, 知樹"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Q-Learningに深層学習を取り入れた手法であるDeep Q-Network（DQN）には，従来のQ-Learningでは扱いきれない画像のような高次元の観測を直接扱うことができるという利点がある。しかし，課題として学習には膨大な回数のエピソードを繰り返す必要がある。この課題に対処するために，別のタスクで学習済みの畳み込みニューラルネットワーク（CNN）を利用した転移学習が行われている。転移学習によりエージェントはタスクに有用な特徴抽出を行える状態から学習を開始できると考えられる。本稿では転移を行うCNNの層数を変化させ，学習回数や得られる報酬にどのような影響が現れるかを実験により調査する。","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"218","bibliographic_titles":[{"bibliographic_title":"第79回全国大会講演論文集"}],"bibliographicPageStart":"217","bibliographicIssueDates":{"bibliographicIssueDate":"2017-03-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2017"}]},"relation_version_is_last":true,"weko_creator_id":"6748"},"created":"2025-01-19T00:48:40.904776+00:00","id":180877,"links":{}}