Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

Variance reduction techniques for gradient estimates in reinforcement learning

dc.contributor.authorGreensmith, Evan
dc.contributor.authorBartlett, Peter L
dc.contributor.authorBaxter, Jonathan
dc.date.accessioned2015-12-13T23:11:25Z
dc.date.available2015-12-13T23:11:25Z
dc.date.issued2004
dc.date.updated2015-12-12T08:27:35Z
dc.identifier.issn0885-6125
dc.identifier.urihttp://hdl.handle.net/1885/87587
dc.publisherKluwer Academic Publishers
dc.sourceMachine Learning
dc.source.urihttp://jmlr.csail.mit.edu/papers/v5
dc.titleVariance reduction techniques for gradient estimates in reinforcement learning
dc.typeJournal article
local.bibliographicCitation.lastpage1530
local.bibliographicCitation.startpage1471
local.contributor.affiliationGreensmith, Evan, College of Engineering and Computer Science, ANU
local.contributor.affiliationBartlett, Peter L, University of California
local.contributor.affiliationBaxter, Jonathan, WhizBang! Labs
local.contributor.authoruidGreensmith, Evan, u4005284
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080199 - Artificial Intelligence and Image Processing not elsewhere classified
local.identifier.ariespublicationMigratedxPub16938
local.identifier.citationvolume5
local.type.statusPublished Version

Downloads