Machine learning for autonomous robots

DC FieldValueLanguage
dc.contributor.authorRiedmiller, M
dc.contributor.editorBiundo, S
dc.contributor.editorFruhwirth, T
dc.contributor.editorPalm, G
dc.date.accessioned2021-12-23T16:00:33Z-
dc.date.available2021-12-23T16:00:33Z-
dc.date.issued2004
dc.identifier.isbn9783540231660
dc.identifier.issn03029743
dc.identifier.urihttps://osnascholar.ub.uni-osnabrueck.de/handle/unios/4453-
dc.description27th Annual German Conference on Artificial Intelligence, Ulm, GERMANY, SEP 20-24, 2004
dc.description.abstractAlthough Reinforcement Learning methods have meanwhile been successfully applied to a wide range of different application scenarios, there is still a lack of methods that would allow the direct application of reinforcement learning to real systems. The key capability of such learning systems is the efficency with respect to the number of interactions with the real system. Several examples are given that illustrate recent progress made in that direction.
dc.language.isoen
dc.publisherSPRINGER-VERLAG BERLIN
dc.relation.ispartofKI 2004: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS
dc.relation.ispartofLECTURE NOTES IN COMPUTER SCIENCE
dc.subjectComputer Science
dc.subjectComputer Science, Artificial Intelligence
dc.titleMachine learning for autonomous robots
dc.typeconference paper
dc.identifier.isiISI:000224604000005
dc.description.volume3238
dc.description.startpage52
dc.description.endpage55
dc.publisher.placeHEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
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