LEADER 01231nam  2200373   450 
001 9910510469903321
005 20230821191056.0
024 7 $a10.1145/3424978
035   $a(CKB)5470000000736667
035   $a(NjHacI)995470000000736667
035   $a(EXLCZ)995470000000736667
100   $a20230821d2020      uy             0
101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aProceedings of the 4th International Conference on Computer Science and Application Engineering /$fAli Emrouznejad
210  1$aNew York, N.Y. :$cAssociation for Computing Machinery,$d2020.
210  4$dİ2020
215   $a1 online resource (1038 pages)
225 0 $aACM Other conferences
311   $a1-4503-7772-6 
320   $aIncludes bibliographical references and index.
606   $aArtificial intelligence
615  0$aArtificial intelligence.
676   $a006.3
700   $aEmrouznejad$b Ali$0993586
801  0$bNjHacI
801  1$bNjHacl
906   $aBOOK
912   $a9910510469903321
996   $aProceedings of the 4th International Conference on Computer Science and Application Engineering$93431631
997   $aUNINA

LEADER 03407nam  22007095  450 
001 9910484119403321
005 20230810172026.0
010   $a3-030-61867-6
024 7 $a10.1007/978-3-030-61867-4
035   $a(CKB)4100000011716924
035   $a(MiAaPQ)EBC6455885
035   $a(DE-He213)978-3-030-61867-4
035   $a(PPN)253254736
035   $a(EXLCZ)994100000011716924
100   $a20210111d2021      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aFrom Shortest Paths to Reinforcement Learning $eA MATLAB-Based Tutorial on Dynamic Programming /$fby Paolo Brandimarte
205   $a1st ed. 2021.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021.
215   $a1 online resource (XI, 207 p. 67 illus.) 
225 1 $aEURO Advanced Tutorials on Operational Research,$x2364-6888
311   $a3-030-61866-8 
327   $aThe dynamic programming principle -- Implementing dynamic programming -- Modeling for dynamic programming -- Numerical dynamic programming for discrete states -- Approximate dynamic programming and reinforcement learning for discrete states -- Numerical dynamic programming for continuous states -- Approximate dynamic programming and reinforcement learning for continuous states.
330   $aDynamic programming (DP) has a relevant history as a powerful and flexible optimization principle, but has a bad reputation as a computationally impractical tool. This book fills a gap between the statement of DP principles and their actual software implementation. Using MATLAB throughout, this tutorial gently gets the reader acquainted with DP and its potential applications, offering the possibility of actual experimentation and hands-on experience. The book assumes basic familiarity with probability and optimization, and is suitable to both practitioners and graduate students in engineering, applied mathematics, management, finance and economics.
410  0$aEURO Advanced Tutorials on Operational Research,$x2364-6888
606   $aOperations research
606   $aManagement science
606   $aEconometrics
606   $aNumerical analysis
606   $aSocial sciences$xMathematics
606   $aIndustrial Management
606   $aOperations Research and Decision Theory
606   $aOperations Research, Management Science 
606   $aQuantitative Economics
606   $aNumerical Analysis
606   $aMathematics in Business, Economics and Finance
606   $aIndustrial Management
615  0$aOperations research.
615  0$aManagement science.
615  0$aEconometrics.
615  0$aNumerical analysis.
615  0$aSocial sciences$xMathematics.
615  0$aIndustrial Management.
615 14$aOperations Research and Decision Theory.
615 24$aOperations Research, Management Science .
615 24$aQuantitative Economics.
615 24$aNumerical Analysis.
615 24$aMathematics in Business, Economics and Finance.
615 24$aIndustrial Management.
676   $a519.703
700   $aBrandimarte$b Paolo$0283971
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bUtOrBLW
906   $aBOOK
912   $a9910484119403321
996   $aFrom shortest paths to reinforcement learning$92850794
997   $aUNINA