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.

Improving the Learning Rate by Inducing a Transition Model

Simple item page
dc.contributor.authorBridle, Robert
dc.contributor.authorMcCreath, Eric
dc.coverage.spatialNew York USA
dc.date.accessioned2015-12-13T22:38:04Z
dc.date.available2015-12-13T22:38:04Z
dc.date.createdJuly 19 2004
dc.date.issued2004
dc.date.updated2015-12-11T09:40:32Z
dc.description.abstractIn general, a reinforcement learning agent requires many trials in order to find a successful policy in a domain. In this paper we investigate inducing a transition model to reduce the number of trials required by an agent. We discuss an approach that incorporates transition model learning within a contemporary agent design.
dc.identifier.urihttp://hdl.handle.net/1885/77385
dc.publisherAssociation for Computing Machinery Inc (ACM)
dc.relation.ispartofseriesInternational Conference on Autonomous Agents and Multiagent Systems (AAMAS 2004)
dc.sourceProceedings of the Third International Joint Conference on Autonomous Agents & Multi Agent Systems (AAMAS 2004)
dc.source.urihttp://satchmo.cs.columbia.edu/aamas04/
dc.source.urihttp://portal.acm.org/citation.cfm?id=1018934&coll=ACM&dl=ACM&CFID=41936345&CFTOKEN=34710039
dc.subjectKeywords: Reinforcement learning agents; Transition models; Approximation theory; Computational complexity; Dynamic programming; Feedback; Learning systems; Mathematical models; Regression analysis; Multi agent systems
dc.titleImproving the Learning Rate by Inducing a Transition Model
dc.typeConference paper
local.bibliographicCitation.lastpage1331
local.bibliographicCitation.startpage1330
local.contributor.affiliationBridle, Robert, College of Engineering and Computer Science, ANU
local.contributor.affiliationMcCreath, Eric, College of Engineering and Computer Science, ANU
local.contributor.authoruidBridle, Robert, u2525222
local.contributor.authoruidMcCreath, Eric, u4033585
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080610 - Information Systems Organisation
local.identifier.ariespublicationMigratedxPub6251
local.identifier.scopusID2-s2.0-4544281945
local.type.statusPublished Version

Downloads

Collections

ANU Research Publications

DSpace software copyright © 2002-2026 LYRASIS