LEADER 01204nas 22003733a 450 001 9910901773403321 005 20240126213016.0 035 $a(DE-599)ZDB2932067-7 035 $a(CKB)110978978740915 035 $a(CONSER)sn-85009252- 035 $a(EXLCZ)99110978978740915 100 $a19830426a19779999 --- a 101 0 $aeng 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAmerican intelligence journal 210 $aAnnapolis, MD $cNational Military Intelligence Association 215 $a1 online resource 311 08$aPrint version: American intelligence journal. 0883-072X (DLC)sn-85009252- (OCoLC)60628288 531 0 $aAm. intell. j. 606 $aIntelligence service$vPeriodicals 606 $aIntelligence service$2fast$3(OCoLC)fst00975848 608 $aPeriodicals.$2fast 608 $aPeriodicals.$2lcgft 615 0$aIntelligence service 615 7$aIntelligence service. 676 $a355 712 02$aNational Military Intelligence Association (U.S.) 906 $aJOURNAL 912 $a9910901773403321 920 $aexl_impl conversion 996 $aAmerican intelligence journal$94273313 997 $aUNINA LEADER 01731nam 2200373z- 450 001 9910346911203321 005 20210211 010 $a1-000-01979-9 035 $a(CKB)4920000000101410 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/45907 035 $a(oapen)doab45907 035 $a(EXLCZ)994920000000101410 100 $a20202102d2010 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aEfficient Reinforcement Learning using Gaussian Processes 210 $cKIT Scientific Publishing$d2010 215 $a1 online resource (IX, 205 p. p.) 225 1 $aKarlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, Intelligent Sensor-Actuator-Systems Laboratory 311 08$a3-86644-569-5 330 $aThis book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO takes model uncertainties consistently into account during long-term planning to reduce model bias. Second, we propose principled algorithms for robust filtering and smoothing in GP dynamic systems. 610 $aautonomous learning 610 $aBayesian inference 610 $acontrol 610 $aGaussian processes 610 $amachine learning 700 $aDeisenroth$b Marc Peter$4auth$01295433 906 $aBOOK 912 $a9910346911203321 996 $aEfficient Reinforcement Learning using Gaussian Processes$93023440 997 $aUNINA LEADER 01913nam0 22004453i 450 001 NAP0148774 005 20251003044245.0 020 $aIT$b1952 7072 090 $a332724 100 $a19990218d1952 ||||0itac50 ba 101 | $aita 102 $ait 181 1$6z01$ai $bxxxe 182 1$6z01$an 183 1$6z01$anc$2RDAcarrier 200 1 $aˆIl ‰mio credo$fDavid E. Lilienthal$gtraduzione di Maria De Sanna 210 $aRoma$cAstrolabio$d1952 215 $a156 p.$d22 cm. 500 10$aThis I do believe. -$3NAP0148778$9IEIV023210$94176347 606 $aDEMOCRAZIA$xStati Uniti d'America$xSaggi$2FIR$3NAPC068023$9I 606 $aDemocrazia$xStati Uniti d'America$2FIR$3CFIC081601$9E 676 $a320.5$9SCIENZA POLITICA. IDEOLOGIE$v22 676 $a321.8$9FORME DEMOCRATICHE$v21 676 $a324.273$9$v21 676 $a324.273$9PARTITI POLITICI. Stati Uniti$v23 686 $aUS/342.0$cPOLITICA E CULTURA STATI UNITI D'AMERICA 1945-1968$2R 686 $aX09.4$cSCIENZE POLITICHE POLITOLOGIA$2R 686 $aX30.9$cTECNOLOGIA E RICERCA SCIENTIFICA$2R 700 1$aLilienthal$b, David Eli$f <1899-1981>$3IEIV023210$4070$0197467 702 1$aDe Sanna$b, Maria$3SBLV216462$4730 801 3$aIT$bIT-000000$c19990218 850 $aIT-BN0095 $aIT-AV0007 $aIT-NA0079 901 $bNAP BN$cA $nComprende volumi con collocazioni per formato catalogati prima del 1987. La consegna del doc. é effettuata dall'Uff. Distribuzione. 901 $bNAP AV$cSEZ. M $nSezione Moderna 901 $bNAP 01$cDEMARCO $n$ 912 $aNAP0148774 950 0$aBiblioteca Centralizzata di Ateneo$b1 v.$c1 v.$d 01DEMARCO MON. 52$e 01 0000097255 VMA 1 v.$fA $h20230504$i20240716 977 $a 01$a AV$a BN 996 $aThis I do believe$94176347 997 $aUNISANNIO