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[null,"1.","Record Nr.",null,"UNISA996394429603316"]
[null,null,"Autore",null,"Hunnis William <d. 1597.>"]
[null,null,"Titolo",null,"[Seuen sobs of a sorrowfull soule for sinne] [[electronic resource]]"]
[null,null,"Pubbl/distr/stampa",null,"[London, : A. Islip for the Company of Stationers, 1629]"]
[null,null,"Descrizione fisica",null,"[8], 85, [3], 95, [1] p. : letterpress music"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Title and imprint from STC."]
[{"$nulls":4},"Signatures: A-H¹² ."]
[{"$nulls":4},"\"A handfull of honisuckles\" has separate title page and begins new pagination; within this series, \"The poore vvidowes mite\" and \"An addition to the treatise a- [sic] aforegoing\" each has separate title page; register is continuous."]
[{"$nulls":4},"The first leaf is blank."]
[{"$nulls":4},"Imperfect; lacks title page."]
[{"$nulls":4},"Reproduction of the original in the Folger Shakespeare Library."]
[null,null,null,"Sommario/riassunto","eebo-0055"]
[null,"2.","Record Nr.",null,"UNISALENTO991004403228307536"]
[null,null,"Autore",null,"Zhao, Shiyu"]
[null,null,"Titolo",null,"Mathematical foundations of reinforcement learning / by Shiyu Zhao"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : Springer Nature Singapore"]
[{"$nulls":4},"Tsinghua : Tsinghua University Press, 2025"]
[null,null,"ISBN",null,"9789819739448"]
[null,null,"Descrizione fisica",null,"xvi, 275 p. ; 25 cm"]
[null,null,"Classificazione",null,"AMS 68T"]
[null,null,"Classificazione LOC",null,"Q334-342"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Artificial intelligence - Data processing"]
[{"$nulls":4},"Multiagent systems"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"1 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"]
[null,null,null,"Sommario/riassunto","This 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"]