EUR 47,11
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Aggiungi al carrelloHardcover. Condizione: As New. No Jacket. Pages are clean and are not marred by notes or folds of any kind. ~ ThriftBooks: Read More, Spend Less 1.42.
Da: Michener & Rutledge Booksellers, Inc., Baldwin City, KS, U.S.A.
EUR 42,13
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Aggiungi al carrelloHardcover. Condizione: As New. Text clean and tight; no dust jacket; Adaptive Computation And Machine Learning; 8vo 8" - 9" tall; 384 pages.
EUR 55,45
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EUR 57,77
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective.The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective.Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common approach to reinforcement learning and expected values, it focuses on the total reward or return obtained as a consequence of an agent's choices-specifically, how this return behaves from a probabilistic perspective. In this first comprehensive guide to distributional reinforcement learning, Marc G. Bellemare, Will Dabney, and Mark Rowland, who spearheaded development of the field, present its key concepts and review some of its many applications. They demonstrate its power to account for many complex, interesting phenomena that arise from interactions with one's environment.The authors present core ideas from classical reinforcement learning to contextualize distributional topics and include mathematical proofs pertaining to major results discussed in the text. They guide the reader through a series of algorithmic and mathematical developments that, in turn, characterize, compute, estimate, and make decisions on the basis of the random return. Practitioners in disciplines as diverse as finance (risk management), computational neuroscience, computational psychiatry, psychology, macroeconomics, and robotics are already using distributional reinforcement learning, paving the way for its expanding applications in mathematical finance, engineering, and the life sciences. More than a mathematical approach, distributional reinforcement learning represents a new perspective on how intelligent agents make predictions and decisions. "Distributional reinforcement learning provides a mathematical theory to describe the random outcomes caused by an agent's decisions"-- Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
EUR 59,06
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Da: Books Puddle, New York, NY, U.S.A.
EUR 56,91
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EUR 61,68
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Aggiungi al carrelloCondizione: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
EUR 57,71
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Aggiungi al carrelloHRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Da: Majestic Books, Hounslow, Regno Unito
EUR 56,22
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EUR 63,89
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Da: ALLBOOKS1, Direk, SA, Australia
EUR 70,21
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Aggiungi al carrelloBrand new book. Fast ship. Please provide full street address as we are not able to ship to P O box address.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 58,42
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EUR 65,38
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EUR 74,21
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Aggiungi al carrelloHardback. Condizione: New.
EUR 57,70
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 62,05
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Da: Chiron Media, Wallingford, Regno Unito
EUR 58,60
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EUR 59,88
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EUR 63,30
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Aggiungi al carrelloHardback. Condizione: New. New copy - Usually dispatched within 4 working days. 526.
Da: Revaluation Books, Exeter, Regno Unito
EUR 53,51
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Aggiungi al carrelloHardcover. Condizione: Brand New. 370 pages. 9.00x6.00x1.00 inches. In Stock.
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 70,98
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EUR 85,82
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EUR 92,55
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Aggiungi al carrelloHardcover. Condizione: New. Brand New! Fast Delivery Textbooks and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 7-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
EUR 88,11
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EUR 50,28
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EUR 67,91
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Aggiungi al carrelloCondizione: New. Marc G. Bellemare is Senior Staff Research Scientist, Google Research and Adjunct Professor, McGill University. Will Dabney is Senior Staff Research Scientist, DeepMind. Mark Rowland is Senior Research Scientist, DeepMind.&ldquoThis important.
EUR 87,77
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EUR 100,34
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective.The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective.Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common approach to reinforcement learning and expected values, it focuses on the total reward or return obtained as a consequence of an agent's choices-specifically, how this return behaves from a probabilistic perspective. In this first comprehensive guide to distributional reinforcement learning, Marc G. Bellemare, Will Dabney, and Mark Rowland, who spearheaded development of the field, present its key concepts and review some of its many applications. They demonstrate its power to account for many complex, interesting phenomena that arise from interactions with one's environment.The authors present core ideas from classical reinforcement learning to contextualize distributional topics and include mathematical proofs pertaining to major results discussed in the text. They guide the reader through a series of algorithmic and mathematical developments that, in turn, characterize, compute, estimate, and make decisions on the basis of the random return. Practitioners in disciplines as diverse as finance (risk management), computational neuroscience, computational psychiatry, psychology, macroeconomics, and robotics are already using distributional reinforcement learning, paving the way for its expanding applications in mathematical finance, engineering, and the life sciences. More than a mathematical approach, distributional reinforcement learning represents a new perspective on how intelligent agents make predictions and decisions. "Distributional reinforcement learning provides a mathematical theory to describe the random outcomes caused by an agent's decisions"-- Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
EUR 72,10
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Aggiungi al carrelloBuch. Condizione: Neu. Neuware - 'Distributional reinforcement learning provides a mathematical theory to describe the random outcomes caused by an agent's decisions'--.
EUR 68,67
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Aggiungi al carrelloHardback. Condizione: New.