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dc.contributor.authorXu, Zhi
dc.contributor.authorLiu, Shuncheng
dc.contributor.authorWu, Ziniu
dc.contributor.authorChen, Xu
dc.contributor.authorZeng, Kai
dc.contributor.authorZheng, Kai
dc.contributor.authorSu, Han
dc.date.accessioned2022-11-10T17:50:33Z
dc.date.available2022-11-10T17:50:33Z
dc.date.issued2021-10-26
dc.identifier.isbn978-1-4503-8446-9
dc.identifier.urihttps://hdl.handle.net/1721.1/146322
dc.publisherACM|Proceedings of the 30th ACM International Conference on Information and Knowledge Managementen_US
dc.relation.isversionofhttps://doi.org/10.1145/3459637.3482283en_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|Proceedings of the 30th ACM International Conference on Information and Knowledge Managementen_US
dc.titlePATROL: A Velocity Control Framework for Autonomous Vehicle via Spatial-Temporal Reinforcement Learningen_US
dc.typeArticleen_US
dc.identifier.citationXu, Zhi, Liu, Shuncheng, Wu, Ziniu, Chen, Xu, Zeng, Kai et al. 2021. "PATROL: A Velocity Control Framework for Autonomous Vehicle via Spatial-Temporal Reinforcement Learning."
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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.updated2022-11-02T22:12:31Z
dc.language.rfc3066en
dc.rights.holderACM
dspace.date.submission2022-11-02T22:12:32Z
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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