{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00213438","sets":["6164:6165:6210:10734"]},"path":["10734"],"owner":"44499","recid":"213438","title":["Procedural Content Generation for Tower Defense Games:a Preliminary Experiment with Reinforcement Learning"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-11-06"},"_buckets":{"deposit":"c5951f49-1973-4a23-b3d2-31fde391d816"},"_deposit":{"id":"213438","pid":{"type":"depid","value":"213438","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Procedural Content Generation for Tower Defense Games:a Preliminary Experiment with Reinforcement Learning","author_link":["546145","546143","546146","546144"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Procedural Content Generation for Tower Defense Games:a Preliminary Experiment with Reinforcement Learning"},{"subitem_title":"Procedural Content Generation for Tower Defense Games:a Preliminary Experiment with Reinforcement Learning","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Procedural Content Generation","subitem_subject_scheme":"Other"},{"subitem_subject":"Reinforcement Learning","subitem_subject_scheme":"Other"},{"subitem_subject":"Tower Defense","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2021-11-06","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Arts and Sciences, The University of Tokyo"},{"subitem_text_value":"Information Technology Center, The University of Tokyo"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Arts and Sciences, The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Information Technology Center, The University of Tokyo","subitem_text_language":"en"}]},"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/213438/files/IPSJ-GPWS2021017.pdf","label":"IPSJ-GPWS2021017.pdf"},"date":[{"dateType":"Available","dateValue":"2021-11-06"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-GPWS2021017.pdf","filesize":[{"value":"1.4 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"18"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"01aae5a6-2944-40ad-be53-795e2651974b","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yueming, Xu"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tetsuro, Tanaka"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yueming, Xu","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tetsuro, Tanaka","creatorNameLang":"en"}],"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_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Although procedural Content Generation via Machine Learning (PCGML) has recently enjoyed consider-able popularity, there is little research on PCGML applied to more complicated games such as Tower Defense (TD). We trained agents to play TD levels (the solver) and generate TD levels (the generator) using reinforcement learning on a TD simulator developed in-house. We conducted the experiments of solver agents with different action spaces to find the most proper one for our task. Then we tried to generate levels, but the results showed that there is still a lot of room for improvement.","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Although procedural Content Generation via Machine Learning (PCGML) has recently enjoyed consider-able popularity, there is little research on PCGML applied to more complicated games such as Tower Defense (TD). We trained agents to play TD levels (the solver) and generate TD levels (the generator) using reinforcement learning on a TD simulator developed in-house. We conducted the experiments of solver agents with different action spaces to find the most proper one for our task. Then we tried to generate levels, but the results showed that there is still a lot of room for improvement.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"97","bibliographic_titles":[{"bibliographic_title":"ゲームプログラミングワークショップ2021論文集"}],"bibliographicPageStart":"93","bibliographicIssueDates":{"bibliographicIssueDate":"2021-11-06","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2021"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":213438,"updated":"2025-01-19T17:09:42.767540+00:00","links":{},"created":"2025-01-19T01:14:18.981842+00:00"}