Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/49523
DC FieldValueLanguage
dc.contributor.authorDubbioso, S.it
dc.contributor.authorDe Tommasi, G.it
dc.contributor.authorMele, Adrianoit
dc.contributor.authorTartaglione, G.it
dc.contributor.authorAriola, M.it
dc.contributor.authorPironti, A.it
dc.contributor.authorMeleit
dc.date.accessioned2023-04-12T22:49:53Z-
dc.date.available2023-04-12T22:49:53Z-
dc.date.issued2023it
dc.identifier.issn09203796it
dc.identifier.urihttp://hdl.handle.net/2067/49523-
dc.language.isoengit
dc.titleA Deep Reinforcement Learning approach for Vertical Stabilization of tokamak plasmasit
dc.typearticle*
dc.identifier.doi10.1016/j.fusengdes.2023.113725it
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0920379623003083?dgcid=coauthorit
dc.relation.journalFUSION ENGINEERING AND DESIGNit
dc.relation.firstpage113725it
dc.relation.volume194it
dc.type.miur262*
item.openairetypearticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.grantfulltextrestricted-
item.languageiso639-1en-
item.fulltextWith Fulltext-
crisitem.journal.journalissn0920-3796-
crisitem.journal.anceE068632-
Appears in Collections:A1. Articolo in rivista
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