Editore: Cambridge University Press, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: Basi6 International, Irving, TX, U.S.A.
EUR 63,27
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Aggiungi al carrelloCondizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
Editore: Cambridge University Press, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
EUR 64,95
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Aggiungi al carrelloHRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Editore: Cambridge University Press, Cambridge, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: San Francisco Book Company, Paris, Francia
EUR 60,00
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Aggiungi al carrelloHardcover. Condizione: Very good. Hardcover Octavo. illustrated boards, 435 pp Standard shipping (no tracking or insurance) / Priority (with tracking) / Custom quote for large or heavy orders.
Editore: Cambridge University Press, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 60,90
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Editore: Cambridge University Press, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: Books Puddle, New York, NY, U.S.A.
EUR 70,85
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Editore: Cambridge University Press, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: California Books, Miami, FL, U.S.A.
EUR 71,58
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Editore: Cambridge University Press, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: Majestic Books, Hounslow, Regno Unito
EUR 70,07
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Editore: Cambridge University Press, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 70,07
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Editore: Cambridge University Press, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 78,16
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Aggiungi al carrelloCondizione: New. 2022. New. Hardcover. . . . . .
Editore: Cambridge University Press, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: Revaluation Books, Exeter, Regno Unito
EUR 70,17
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Aggiungi al carrelloHardcover. Condizione: Brand New. 435 pages. 9.75x6.75x1.00 inches. In Stock.
Editore: Cambridge University Press, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 64,94
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Editore: Cambridge University Press, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 72,40
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Aggiungi al carrelloGebunden. Condizione: New. The book is written for newcomers to reinforcement learning who wish to write code for various applications, from robotics to power systems to supply chains. It also contains advanced material designed to prepare graduate students and professionals for both.
Editore: Cambridge University Press, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 68,99
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Aggiungi al carrelloHardback. Condizione: New. New copy - Usually dispatched within 4 working days. 1041.
Editore: Cambridge University Press, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 74,98
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Editore: Cambridge University Press, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 71,90
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Editore: Cambridge University Press, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 72,21
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Editore: Cambridge University Press Jun 2022, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 76,16
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Aggiungi al carrelloBuch. Condizione: Neu. Neuware - 'A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning'--.
Editore: Cambridge University Press, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: Kennys Bookstore, Olney, MD, U.S.A.
EUR 96,05
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Aggiungi al carrelloCondizione: New. 2022. New. Hardcover. . . . . . Books ship from the US and Ireland.
Editore: Cambridge University Press, Cambridge, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: CitiRetail, Stevenage, Regno Unito
EUR 72,34
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning. The book is written for newcomers to reinforcement learning who wish to write code for various applications, from robotics to power systems to supply chains. It also contains advanced material designed to prepare graduate students and professionals for both research and application of reinforcement learning and optimal control techniques. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Editore: Cambridge University Press, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: Revaluation Books, Exeter, Regno Unito
EUR 100,69
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Aggiungi al carrelloHardcover. Condizione: Brand New. 435 pages. 9.75x6.75x1.00 inches. In Stock.
Editore: Cambridge University Press, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 63,57
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Aggiungi al carrelloCondizione: New.
Editore: Cambridge University Press, Cambridge, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: Grand Eagle Retail, Fairfield, OH, U.S.A.
EUR 77,59
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: new. Hardcover. A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning. The book is written for newcomers to reinforcement learning who wish to write code for various applications, from robotics to power systems to supply chains. It also contains advanced material designed to prepare graduate students and professionals for both research and application of reinforcement learning and optimal control techniques. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Editore: Cambridge University Press, Cambridge, 2022
ISBN 10: 1316511960 ISBN 13: 9781316511961
Lingua: Inglese
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 125,83
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloHardcover. Condizione: new. Hardcover. A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning. The book is written for newcomers to reinforcement learning who wish to write code for various applications, from robotics to power systems to supply chains. It also contains advanced material designed to prepare graduate students and professionals for both research and application of reinforcement learning and optimal control techniques. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.