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Verifiably safe exploration for end-to-end reinforcement learning

dc.contributor.authorHunt, Nathan
dc.contributor.authorFulton, Nathan
dc.contributor.authorMagliacane, Sara
dc.contributor.authorHoang, Trong Nghia
dc.contributor.authorDas, Subhro
dc.contributor.authorSolar-Lezama, Armando
dc.date.accessioned2022-07-20T15:40:10Z
dc.date.available2022-07-20T15:40:10Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/1721.1/143887
dc.language.isoen
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.isversionof10.1145/3447928.3456653en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceACMen_US
dc.titleVerifiably safe exploration for end-to-end reinforcement learningen_US
dc.typeArticleen_US
dc.identifier.citationHunt, Nathan, Fulton, Nathan, Magliacane, Sara, Hoang, Trong Nghia, Das, Subhro et al. 2021. "Verifiably safe exploration for end-to-end reinforcement learning." Proceedings of the 24th International Conference on Hybrid Systems: Computation and Control.
dc.contributor.departmentMIT-IBM Watson AI Lab
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.relation.journalProceedings of the 24th International Conference on Hybrid Systems: Computation and Controlen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-07-20T15:25:06Z
dspace.orderedauthorsHunt, N; Fulton, N; Magliacane, S; Hoang, TN; Das, S; Solar-Lezama, Aen_US
dspace.date.submission2022-07-20T15:25:07Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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