A Review of the Deep Sea Treasure problem as a Multi-Objective Reinforcement Learning Benchmark
In this paper, the authors investigate the Deep Sea Treasure (DST) problem as proposed by Vamplew et al. Through a number of proofs, the authors show the original DST problem to be quite basic, and not always representative of practical Multi-Objective Optimization problems. In an attempt to bring t...
Gespeichert in:
| Veröffentlicht in: | arXiv.org 2024-05 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | arXiv.org |
| container_volume | |
| creator | Cassimon, Amber Eyckerman, Reinout Mercelis, Siegfried Latré, Steven Hellinckx, Peter |
| description | In this paper, the authors investigate the Deep Sea Treasure (DST) problem as proposed by Vamplew et al. Through a number of proofs, the authors show the original DST problem to be quite basic, and not always representative of practical Multi-Objective Optimization problems. In an attempt to bring theory closer to practice, the authors propose an alternative, improved version of the DST problem, and prove that some of the properties that simplify the original DST problem no longer hold. The authors also provide a reference implementation and perform a comparison between their implementation, and other existing open-source implementations of the problem. Finally, the authors also provide a complete Pareto-front for their new DST problem. |
| format | Article |
| fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2581963670</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2581963670</sourcerecordid><originalsourceid>FETCH-proquest_journals_25819636703</originalsourceid><addsrcrecordid>eNqNjL0KwjAYRYMgKNp3-MC50Cb2x9FfHBRB3Usst5rapjVJ9fXt4AM43eGccwdszIUI_XTO-Yh51pZBEPA44VEkxixb0hlvhQ81BbkHaAO0dIGkq4G0nQG1prlVqElaknTsKqf8061E7tQbfax00ZgcNbSjA6TRSt9pBZ0_ammeUzYsZGXh_XbCZrvtdb33-9dXB-uysumM7lHGozRcxCJOAvGf9QXrjEOS</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2581963670</pqid></control><display><type>article</type><title>A Review of the Deep Sea Treasure problem as a Multi-Objective Reinforcement Learning Benchmark</title><source>Free E- Journals</source><creator>Cassimon, Amber ; Eyckerman, Reinout ; Mercelis, Siegfried ; Latré, Steven ; Hellinckx, Peter</creator><creatorcontrib>Cassimon, Amber ; Eyckerman, Reinout ; Mercelis, Siegfried ; Latré, Steven ; Hellinckx, Peter</creatorcontrib><description>In this paper, the authors investigate the Deep Sea Treasure (DST) problem as proposed by Vamplew et al. Through a number of proofs, the authors show the original DST problem to be quite basic, and not always representative of practical Multi-Objective Optimization problems. In an attempt to bring theory closer to practice, the authors propose an alternative, improved version of the DST problem, and prove that some of the properties that simplify the original DST problem no longer hold. The authors also provide a reference implementation and perform a comparison between their implementation, and other existing open-source implementations of the problem. Finally, the authors also provide a complete Pareto-front for their new DST problem.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Deep sea environments ; Multiple objective analysis ; Pareto optimization</subject><ispartof>arXiv.org, 2024-05</ispartof><rights>2024. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>780,784</link.rule.ids></links><search><creatorcontrib>Cassimon, Amber</creatorcontrib><creatorcontrib>Eyckerman, Reinout</creatorcontrib><creatorcontrib>Mercelis, Siegfried</creatorcontrib><creatorcontrib>Latré, Steven</creatorcontrib><creatorcontrib>Hellinckx, Peter</creatorcontrib><title>A Review of the Deep Sea Treasure problem as a Multi-Objective Reinforcement Learning Benchmark</title><title>arXiv.org</title><description>In this paper, the authors investigate the Deep Sea Treasure (DST) problem as proposed by Vamplew et al. Through a number of proofs, the authors show the original DST problem to be quite basic, and not always representative of practical Multi-Objective Optimization problems. In an attempt to bring theory closer to practice, the authors propose an alternative, improved version of the DST problem, and prove that some of the properties that simplify the original DST problem no longer hold. The authors also provide a reference implementation and perform a comparison between their implementation, and other existing open-source implementations of the problem. Finally, the authors also provide a complete Pareto-front for their new DST problem.</description><subject>Deep sea environments</subject><subject>Multiple objective analysis</subject><subject>Pareto optimization</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNjL0KwjAYRYMgKNp3-MC50Cb2x9FfHBRB3Usst5rapjVJ9fXt4AM43eGccwdszIUI_XTO-Yh51pZBEPA44VEkxixb0hlvhQ81BbkHaAO0dIGkq4G0nQG1prlVqElaknTsKqf8061E7tQbfax00ZgcNbSjA6TRSt9pBZ0_ammeUzYsZGXh_XbCZrvtdb33-9dXB-uysumM7lHGozRcxCJOAvGf9QXrjEOS</recordid><startdate>20240521</startdate><enddate>20240521</enddate><creator>Cassimon, Amber</creator><creator>Eyckerman, Reinout</creator><creator>Mercelis, Siegfried</creator><creator>Latré, Steven</creator><creator>Hellinckx, Peter</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20240521</creationdate><title>A Review of the Deep Sea Treasure problem as a Multi-Objective Reinforcement Learning Benchmark</title><author>Cassimon, Amber ; Eyckerman, Reinout ; Mercelis, Siegfried ; Latré, Steven ; Hellinckx, Peter</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_25819636703</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Deep sea environments</topic><topic>Multiple objective analysis</topic><topic>Pareto optimization</topic><toplevel>online_resources</toplevel><creatorcontrib>Cassimon, Amber</creatorcontrib><creatorcontrib>Eyckerman, Reinout</creatorcontrib><creatorcontrib>Mercelis, Siegfried</creatorcontrib><creatorcontrib>Latré, Steven</creatorcontrib><creatorcontrib>Hellinckx, Peter</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cassimon, Amber</au><au>Eyckerman, Reinout</au><au>Mercelis, Siegfried</au><au>Latré, Steven</au><au>Hellinckx, Peter</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>A Review of the Deep Sea Treasure problem as a Multi-Objective Reinforcement Learning Benchmark</atitle><jtitle>arXiv.org</jtitle><date>2024-05-21</date><risdate>2024</risdate><eissn>2331-8422</eissn><abstract>In this paper, the authors investigate the Deep Sea Treasure (DST) problem as proposed by Vamplew et al. Through a number of proofs, the authors show the original DST problem to be quite basic, and not always representative of practical Multi-Objective Optimization problems. In an attempt to bring theory closer to practice, the authors propose an alternative, improved version of the DST problem, and prove that some of the properties that simplify the original DST problem no longer hold. The authors also provide a reference implementation and perform a comparison between their implementation, and other existing open-source implementations of the problem. Finally, the authors also provide a complete Pareto-front for their new DST problem.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | EISSN: 2331-8422 |
| ispartof | arXiv.org, 2024-05 |
| issn | 2331-8422 |
| language | eng |
| recordid | cdi_proquest_journals_2581963670 |
| source | Free E- Journals |
| subjects | Deep sea environments Multiple objective analysis Pareto optimization |
| title | A Review of the Deep Sea Treasure problem as a Multi-Objective Reinforcement Learning Benchmark |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-26T09%3A48%3A47IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=A%20Review%20of%20the%20Deep%20Sea%20Treasure%20problem%20as%20a%20Multi-Objective%20Reinforcement%20Learning%20Benchmark&rft.jtitle=arXiv.org&rft.au=Cassimon,%20Amber&rft.date=2024-05-21&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2581963670%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2581963670&rft_id=info:pmid/&rfr_iscdi=true |