The data of LazyAct.
<p>In reinforcement learning tasks, data is generated from the environment code.</p> <p>(ZIP)</p>
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2025
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| _version_ | 1852022957893746688 |
|---|---|
| author | Hongjie Zhang (136127) |
| author2 | Zhenyu Chen (2359471) Hourui Deng (20685396) Chaosheng Feng (20685399) |
| author2_role | author author author |
| author_facet | Hongjie Zhang (136127) Zhenyu Chen (2359471) Hourui Deng (20685396) Chaosheng Feng (20685399) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hongjie Zhang (136127) Zhenyu Chen (2359471) Hourui Deng (20685396) Chaosheng Feng (20685399) |
| dc.date.none.fl_str_mv | 2025-02-06T18:44:27Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0318778.s002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/The_data_of_LazyAct_/28362909 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Biotechnology Sociology Developmental Biology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified state skipping branch h ?\ rlkey h ?% 5crlkey establish optimization objectives deep reinforcement learning achieved significant success high computational cost div >< p algorithm significantly reduces computational cost utilize pre tuning techniques practical application policies based minimal impact mappo frameworks making tasks making patterns linear increase lazy actor involve reasoning human decision flops required decision made continuous decision complex decision complete tasks approximately 80 actor network |
| dc.title.none.fl_str_mv | The data of LazyAct. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>In reinforcement learning tasks, data is generated from the environment code.</p> <p>(ZIP)</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_bbf13263e21164db462f2e2cb9c253ef |
| identifier_str_mv | 10.1371/journal.pone.0318778.s002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28362909 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The data of LazyAct.Hongjie Zhang (136127)Zhenyu Chen (2359471)Hourui Deng (20685396)Chaosheng Feng (20685399)MedicineBiotechnologySociologyDevelopmental BiologyScience PolicyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedstate skipping branchh ?\ rlkeyh ?% 5crlkeyestablish optimization objectivesdeep reinforcement learningachieved significant successhigh computational costdiv >< palgorithm significantly reducescomputational costutilize pretuning techniquespractical applicationpolicies basedminimal impactmappo frameworksmaking tasksmaking patternslinear increaselazy actorinvolve reasoninghuman decisionflops requireddecision madecontinuous decisioncomplex decisioncomplete tasksapproximately 80actor network<p>In reinforcement learning tasks, data is generated from the environment code.</p> <p>(ZIP)</p>2025-02-06T18:44:27ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0318778.s002https://figshare.com/articles/dataset/The_data_of_LazyAct_/28362909CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/283629092025-02-06T18:44:27Z |
| spellingShingle | The data of LazyAct. Hongjie Zhang (136127) Medicine Biotechnology Sociology Developmental Biology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified state skipping branch h ?\ rlkey h ?% 5crlkey establish optimization objectives deep reinforcement learning achieved significant success high computational cost div >< p algorithm significantly reduces computational cost utilize pre tuning techniques practical application policies based minimal impact mappo frameworks making tasks making patterns linear increase lazy actor involve reasoning human decision flops required decision made continuous decision complex decision complete tasks approximately 80 actor network |
| status_str | publishedVersion |
| title | The data of LazyAct. |
| title_full | The data of LazyAct. |
| title_fullStr | The data of LazyAct. |
| title_full_unstemmed | The data of LazyAct. |
| title_short | The data of LazyAct. |
| title_sort | The data of LazyAct. |
| topic | Medicine Biotechnology Sociology Developmental Biology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified state skipping branch h ?\ rlkey h ?% 5crlkey establish optimization objectives deep reinforcement learning achieved significant success high computational cost div >< p algorithm significantly reduces computational cost utilize pre tuning techniques practical application policies based minimal impact mappo frameworks making tasks making patterns linear increase lazy actor involve reasoning human decision flops required decision made continuous decision complex decision complete tasks approximately 80 actor network |