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dc.contributor.authorKim, Dong-Ki
dc.contributor.authorLiu, Miao
dc.contributor.authorRiemer, Matthew
dc.contributor.authorSun, Chuangchuang
dc.contributor.authorAbdulhai, Marwa
dc.contributor.authorHabibi, Golnaz
dc.contributor.authorLopez-Cot, Sebastian
dc.contributor.authorTesauro, Gerald
dc.contributor.authorHow, Jonathan P
dc.date.accessioned2022-09-13T15:31:13Z
dc.date.available2022-09-13T15:31:13Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/1721.1/145369
dc.language.isoen
dc.relation.isversionofhttps://proceedings.mlr.press/v139/kim21g.htmlen_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceProceedings of Machine Learning Researchen_US
dc.titleA Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learningen_US
dc.typeArticleen_US
dc.identifier.citationKim, Dong-Ki, Liu, Miao, Riemer, Matthew, Sun, Chuangchuang, Abdulhai, Marwa et al. 2021. "A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning." INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139, 139.
dc.contributor.departmentMassachusetts Institute of Technology. Laboratory for Information and Decision Systems
dc.contributor.departmentMIT-IBM Watson AI Lab
dc.relation.journalINTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139en_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2022-09-13T15:24:24Z
dspace.orderedauthorsKim, D-K; Liu, M; Riemer, M; Sun, C; Abdulhai, M; Habibi, G; Lopez-Cot, S; Tesauro, G; How, JPen_US
dspace.date.submission2022-09-13T15:24:26Z
mit.journal.volume139en_US
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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