LEADER 01084cam0-2200385---450- 001 990004513560403321 005 20140218132346.0 035 $a000451356 035 $aFED01000451356 035 $a(Aleph)000451356FED01 035 $a000451356 100 $a19990604d1957----km-y0itay50------ba 101 2 $aita$arus 102 $aIT 105 $ay-------001fy 200 1 $aRaccolta di favole$d= Sobranie basen$fKrylov$g[a cura di Vera Zdrojewska] 210 $aFirenze$cEd. Fussi$d1957$cSansoni 215 $a223 p.$d18 cm 225 1 $a<>melagrano$v186-189 300 $aAltro front. in russo 300 $aTesto orig. a fronte 454 0$12001 510 1 $aSobranie basen 610 0 $aFavole russe$aAntologie 676 $a398.209 676 $a891.7 700 1$aKrylov,$bIvan Andreevic$f<1768-1844>$0189654 702 1$aZdrojewska,$bVera 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990004513560403321 952 $a398.2 KRY 1$bBibl. 30512$fFLFBC 959 $aFLFBC 996 $aRaccolta di favole$9547738 997 $aUNINA LEADER 02408nam a22003015i 4500 001 991004403228307536 005 20251027151029.0 008 251024s2025 si a r 000 0 eng d 020 $a9789819739448 024 7 $a10.1007/978-981-97-3944-8$2doi 040 $aBibl. Dip.le Aggr. Matematica e Fisica - Sez. Matematica$beng 050 4$aQ334-342 082 04$a006.3 084 $aAMS 68T 100 1 $aZhao, Shiyu$01785025 245 10$aMathematical foundations of reinforcement learning /$cby Shiyu Zhao 260 $aSingapore :$bSpringer Nature Singapore ;$aTsinghua :$bTsinghua University Press,$c2025 300 $axvi, 275 p. ;$c25 cm 505 0 $a1 Basic Concepts -- 2 State Value and Bellman Equation -- 3 Optimal State Value and Bellman Optimality Equation -- 4 Value Iteration and Policy Iteration -- 5 Monte Carlo Learning -- 6 Stochastic Approximation -- 7 Temporal-Difference Learning -- 8 Value Function Approximation -- 9 Policy Gradient -- 10 Actor-Critic Methods 520 $aThis book provides a mathematical yet accessible introduction to the fundamental concepts, core challenges, and classic reinforcement learning algorithms. It aims to help readers understand the theoretical foundations of algorithms, providing insights into their design and functionality. Numerous illustrative examples are included throughout. The mathematical content is carefully structured to ensure readability and approachability. The book is divided into two parts. The first part is on the mathematical foundations of reinforcement learning, covering topics such as the Bellman equation, Bellman optimality equation, and stochastic approximation. The second part explicates reinforcement learning algorithms, including value iteration and policy iteration, Monte Carlo methods, temporal-difference methods, value function methods, policy gradient methods, and actor-critic methods. With its comprehensive scope, the book will appeal to undergraduate and graduate students, post-doctoral researchers, lecturers, industrial researchers, and anyone interested in reinforcement learning 650 4$aArtificial intelligence 650 4$aMachine learning 650 4$aArtificial intelligence$xData processing 650 4$aMultiagent systems 776 08$z9789819739431 912 $a991004403228307536 996 $aMathematical Foundations of Reinforcement Learning$94316642 997 $aUNISALENTO