Multi agent collaboration using distributed value functions
Resumen:
In this paper we present the use of distributed value function techniques to reach collaboration in a multiagent system. We apply this method in two different simulation environments: a mobile robot planning/searching task and an intelligent traffic system in an urban environment. In the case of the intelligent traffic system, results show an improvement with respect to a standard fix-time controller and local adaptive controllers. Trajectories for optimal search in an obstacle environment are obtained in the mobile robot case. Some variations to the actual algorithm are pointed out to suit our cases. We conclude discussing our future work.
| 2000 | |
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Reinforcement learning Distributed system Mobile robot Traffic control |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/20811 | |
| Acceso abierto |
| _version_ | 1807522929440718848 |
|---|---|
| author | Ferreira, Enrique |
| author2 | Khosla, P |
| author2_role | author |
| author_facet | Ferreira, Enrique Khosla, P |
| author_role | author |
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| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Ferreira, Enrique Khosla, P |
| dc.date.accessioned.none.fl_str_mv | 2019-05-29T15:28:22Z |
| dc.date.available.none.fl_str_mv | 2019-05-29T15:28:22Z |
| dc.date.issued.es.fl_str_mv | 2000 |
| dc.date.submitted.es.fl_str_mv | 20190528 |
| dc.description.abstract.none.fl_txt_mv | In this paper we present the use of distributed value function techniques to reach collaboration in a multiagent system. We apply this method in two different simulation environments: a mobile robot planning/searching task and an intelligent traffic system in an urban environment. In the case of the intelligent traffic system, results show an improvement with respect to a standard fix-time controller and local adaptive controllers. Trajectories for optimal search in an obstacle environment are obtained in the mobile robot case. Some variations to the actual algorithm are pointed out to suit our cases. We conclude discussing our future work. |
| dc.identifier.citation.es.fl_str_mv | Ferreira, Enrique, Khosla, P. Multi agent collaboration using distributed value functions [en línea] IEEE Intelligent Vehicles Symposium 2000. |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/20811 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | IEEE |
| dc.relation.ispartof.es.fl_str_mv | IEEE Intelligent Vehicles Symposium 2000. Proceedings. |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.es.fl_str_mv | Reinforcement learning Distributed system Mobile robot Traffic control |
| dc.title.none.fl_str_mv | Multi agent collaboration using distributed value functions |
| dc.type.es.fl_str_mv | Artículo |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/article |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | In this paper we present the use of distributed value function techniques to reach collaboration in a multiagent system. We apply this method in two different simulation environments: a mobile robot planning/searching task and an intelligent traffic system in an urban environment. In the case of the intelligent traffic system, results show an improvement with respect to a standard fix-time controller and local adaptive controllers. Trajectories for optimal search in an obstacle environment are obtained in the mobile robot case. Some variations to the actual algorithm are pointed out to suit our cases. We conclude discussing our future work. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | COLIBRI_3d022231ac277012dcf14dd734da1b3d |
| identifier_str_mv | Ferreira, Enrique, Khosla, P. Multi agent collaboration using distributed value functions [en línea] IEEE Intelligent Vehicles Symposium 2000. |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/20811 |
| publishDate | 2000 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| spelling | 2019-05-29T15:28:22Z2019-05-29T15:28:22Z200020190528Ferreira, Enrique, Khosla, P. Multi agent collaboration using distributed value functions [en línea] IEEE Intelligent Vehicles Symposium 2000.https://hdl.handle.net/20.500.12008/20811In this paper we present the use of distributed value function techniques to reach collaboration in a multiagent system. We apply this method in two different simulation environments: a mobile robot planning/searching task and an intelligent traffic system in an urban environment. In the case of the intelligent traffic system, results show an improvement with respect to a standard fix-time controller and local adaptive controllers. Trajectories for optimal search in an obstacle environment are obtained in the mobile robot case. Some variations to the actual algorithm are pointed out to suit our cases. We conclude discussing our future work.Made available in DSpace on 2019-05-29T15:28:22Z (GMT). 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Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessReinforcement learningDistributed systemMobile robotTraffic controlMulti agent collaboration using distributed value functionsArtículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaFerreira, EnriqueKhosla, PLICENSElicense.txttext/plain4194http://localhost:8080/xmlui/bitstream/20.500.12008/20811/4/license.txt7f2e2c17ef6585de66da58d1bfa8b5e1MD54CC-LICENSElicense_textapplication/octet-stream21936http://localhost:8080/xmlui/bitstream/20.500.12008/20811/1/license_text9833653f73f7853880c94a6fead477b1MD51license_urlapplication/octet-stream49http://localhost:8080/xmlui/bitstream/20.500.12008/20811/2/license_url4afdbb8c545fd630ea7db775da747b2fMD52license_rdfapplication/octet-stream23148http://localhost:8080/xmlui/bitstream/20.500.12008/20811/3/license_rdf9da0b6dfac957114c6a7714714b86306MD5320.500.12008/208112019-10-31 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- Universidad de la Repúblicafalse |
| spellingShingle | Multi agent collaboration using distributed value functions Ferreira, Enrique Reinforcement learning Distributed system Mobile robot Traffic control |
| status_str | publishedVersion |
| title | Multi agent collaboration using distributed value functions |
| title_full | Multi agent collaboration using distributed value functions |
| title_fullStr | Multi agent collaboration using distributed value functions |
| title_full_unstemmed | Multi agent collaboration using distributed value functions |
| title_short | Multi agent collaboration using distributed value functions |
| title_sort | Multi agent collaboration using distributed value functions |
| topic | Reinforcement learning Distributed system Mobile robot Traffic control |
| url | https://hdl.handle.net/20.500.12008/20811 |
