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010   $a3-319-12197-9
024 7 $a10.1007/978-3-319-12197-0
035   $a(CKB)3710000000306150
035   $a(EBL)1968615
035   $a(OCoLC)908090188
035   $a(SSID)ssj0001386221
035   $a(PQKBManifestationID)11814638
035   $a(PQKBTitleCode)TC0001386221
035   $a(PQKBWorkID)11373884
035   $a(PQKB)11102609
035   $a(DE-He213)978-3-319-12197-0
035   $a(MiAaPQ)EBC1968615
035   $a(PPN)183098080
035   $a(EXLCZ)993710000000306150
100   $a20141122d2015      u|         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aDesign of Experiments for Reinforcement Learning /$fby Christopher Gatti
205   $a1st ed. 2015.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015.
215   $a1 online resource (196 p.)
225 1 $aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053
300   $aDescription based upon print version of record.
311   $a3-319-12196-0 
320   $aIncludes bibliographical references at the end of each chapters.
327   $aIntroduction -- Reinforcement Learning. Design of Experiments -- Methodology -- The Mountain Car Problem -- The Truck Backer-Upper Problem -- The Tandem Truck Backer-Upper Problem -- Appendices.
330   $aThis thesis takes an empirical approach to understanding of the behavior and interactions between the two main components of reinforcement learning: the learning algorithm and the functional representation of learned knowledge. The author approaches these entities using design of experiments not commonly employed to study machine learning methods. The results outlined in this work provide insight as to what enables and what has an effect on successful reinforcement learning implementations so that this learning method can be applied to more challenging problems.
410  0$aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053
606   $aComputational intelligence
606   $aLogic design
606   $aArtificial intelligence
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aLogic Design$3https://scigraph.springernature.com/ontologies/product-market-codes/I12050
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
615  0$aComputational intelligence.
615  0$aLogic design.
615  0$aArtificial intelligence.
615 14$aComputational Intelligence.
615 24$aLogic Design.
615 24$aArtificial Intelligence.
676   $a006.3
676   $a620
676   $a621.395
700   $aGatti$b Christopher$4aut$4http://id.loc.gov/vocabulary/relators/aut$0720714
906   $aBOOK
912   $a9910299671203321
996   $aDesign of Experiments for Reinforcement Learning$91412369
997   $aUNINA