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[null,null,"Titolo",null,"Recent Advances in Reinforcement Learning [[electronic resource] ] : 9th European Workshop, EWRL 2011, Athens, Greece, September 9-11, 2011, Revised and Selected Papers / / edited by Scott Sanner, Marcus Hutter"]
[null,null,"Pubbl/distr/stampa",null,"Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2012"]
[null,null,"ISBN",null,"3-642-29946-6"]
[null,null,"Edizione",null,"[1st ed. 2012.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XIII, 345 p. 98 illus.)"]
[null,null,"Collana",null,"Lecture Notes in Artificial Intelligence ; ; 7188"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Computers"]
[{"$nulls":4},"Algorithms"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Database management"]
[{"$nulls":4},"Mathematical statistics"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computation by Abstract Devices"]
[{"$nulls":4},"Algorithm Analysis and Problem Complexity"]
[{"$nulls":4},"Information Systems Applications (incl. Internet)"]
[{"$nulls":4},"Database Management"]
[{"$nulls":4},"Probability and Statistics in Computer Science"]
[{"$nulls":4},"Conference proceedings."]
[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","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and author index."]
[null,null,null,"Sommario/riassunto","This book constitutes revised and selected papers of the 9th European Workshop on Reinforcement Learning, EWRL 2011, which took place in Athens, Greece in September 2011. The papers presented were carefully reviewed and selected from 40 submissions. The papers are organized in topical sections online reinforcement learning, learning and exploring MDPs, function approximation methods for reinforcement learning, macro-actions in reinforcement learning, policy search and bounds, multi-task and transfer reinforcement learning, multi-agent reinforcement learning, apprenticeship and inverse "]
[{"$nulls":4},"reinforcement learning and real-world reinforcement learning."]