LEADER 00818nam0-22003011i-450- 001 990003462190403321 005 20001010 035 $a000346219 035 $aFED01000346219 035 $a(Aleph)000346219FED01 035 $a000346219 100 $a20000920d1893----km-y0itay50------ba 101 0 $aita 105 $ay-------001yy 200 1 $aCharles Rogier (1800-1885) d'après des docu ments inédits 210 $aBruxelles$cLebégue$d1893 215 $a446 225 1 $a 700 1$aDiscailles Ernest$0133961 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990003462190403321 952 $aSE 064.06.04-$fDECSE 952 $aSE 064.06.04-$fDECSE 959 $aDECSE 996 $aCharles Rogier (1800-1885) d'après des docu ments inédits$9445428 997 $aUNINA DB $aING01 LEADER 01202nam--2200397---450- 001 990000876860203316 005 20050712125148.0 010 $a0-7450-0823-2 035 $a0087686 035 $aUSA010087686 035 $a(ALEPH)000087686USA01 035 $a0087686 100 $a20020116d1990----km-y0itay0103----ba 101 $aeng 102 $aUS 105 $a||||||||001yy 200 1 $aVirginia Woolf's To the lighthouse$fSuzanne Raitt 210 $aNew York$cHarvester Wheatsheaft$d1990 215 $aXIV, 129 p$d20 cm 225 2 $aCritical studies of key texts 410 $12001$aCritical studies of key texts 606 0 $aWoolf, Virginia$xTo the lighthouse 676 $a823.912 700 1$aRAITT,$bSuzanne$0221523 801 0$aIT$bsalbc$gISBD 912 $a990000876860203316 951 $aVII.3.B. 618(II i A 871)$b104196 LM$cII i A 959 $aBK 969 $aUMA 979 $aPATTY$b90$c20020116$lUSA01$h1034 979 $c20020403$lUSA01$h1732 979 $aPATTY$b90$c20020408$lUSA01$h1530 979 $aPATRY$b90$c20040406$lUSA01$h1700 979 $aCOPAT3$b90$c20050712$lUSA01$h1251 996 $aVirginia Woolf's To the lighthouse$9969875 997 $aUNISA 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