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dc.contributor.authorZhao, Ying
dc.contributor.authorHemberg, Erik
dc.contributor.authorDerbinsky, Nate
dc.contributor.authorMata, Gabino
dc.contributor.authorO'Reilly, Una-May
dc.date.accessioned2022-11-10T18:29:29Z
dc.date.available2022-11-10T18:29:29Z
dc.date.issued2022-07-09
dc.identifier.isbn978-1-4503-9268-6
dc.identifier.urihttps://hdl.handle.net/1721.1/146337
dc.publisherACM|Genetic and Evolutionary Computation Conference Companionen_US
dc.relation.isversionofhttps://doi.org/10.1145/3520304.3528990en_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.sourceACM|Genetic and Evolutionary Computation Conference Companionen_US
dc.titleUsing Domain Knowledge in Coevolution and Reinforcement Learning to Simulate a Logistics Enterpriseen_US
dc.typeArticleen_US
dc.identifier.citationZhao, Ying, Hemberg, Erik, Derbinsky, Nate, Mata, Gabino and O'Reilly, Una-May. 2022. "Using Domain Knowledge in Coevolution and Reinforcement Learning to Simulate a Logistics Enterprise."
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.identifier.mitlicensePUBLISHER_POLICY
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.updated2022-11-03T01:07:54Z
dc.language.rfc3066en
dc.rights.holderThe author(s)
dspace.date.submission2022-11-03T01:07:54Z
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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