Editore: Addison Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
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Editore: Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
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Editore: Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Lingua: Inglese
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Editore: Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
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Editore: Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
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Editore: Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
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Aggiungi al carrelloCondizione: New. Über den AutorLaura Graesser is a research software engineer working in robotics at Google. She holds a master s degree in computer science from New York University, where she specialized in machine learning.
Editore: Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Lingua: Inglese
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 48,39
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Editore: Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Lingua: Inglese
Da: Revaluation Books, Exeter, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: Brand New. 379 pages. 9.00x7.00x0.50 inches. In Stock.
Editore: Pearson Education Dez 2019, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware - In just a few years, deep reinforcement learning (DRL) systems such as DeepMinds DQN have yielded remarkable results. This hybrid approach to machine learning shares many similarities with human learning: its unsupervised self-learning, self-discovery of strategies, usage of memory, balance of exploration and exploitation, and its exceptional flexibility. Exciting in its own right, DRL may presage even more remarkable advances in general artificial intelligence.
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Editore: Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Lingua: Inglese
Da: Books Puddle, New York, NY, U.S.A.
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Editore: Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
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Da: Revaluation Books, Exeter, Regno Unito
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Aggiungi al carrelloPaperback. Condizione: Brand New. 379 pages. 9.00x7.00x0.50 inches. In Stock.
Editore: Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Lingua: Inglese
Da: Books for Life, LAUREL, MD, U.S.A.
EUR 26,62
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Aggiungi al carrelloCondizione: very_good. Book is in very good condition. Clean with little to no signs of wear or markings highlights.
Editore: Pearson Education (US), Boston, 2020
ISBN 10: 0135172381 ISBN 13: 9780135172384
Lingua: Inglese
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 77,93
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games - such as Go, Atari games, and DotA 2 - to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. Understand each key aspect of a deep RL problem Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) Understand how algorithms can be parallelised synchronously and asynchronously Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work Explore algorithm benchmark results with tuned hyperparameters Understand how deep RL environments are designedThis guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Editore: Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Lingua: Inglese
Da: HPB-Red, Dallas, TX, U.S.A.
EUR 24,13
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Aggiungi al carrelloPaperback. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
Editore: Pearson Education (US), Boston, 2020
ISBN 10: 0135172381 ISBN 13: 9780135172384
Lingua: Inglese
Da: Grand Eagle Retail, Fairfield, OH, U.S.A.
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games - such as Go, Atari games, and DotA 2 - to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. Understand each key aspect of a deep RL problem Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) Understand how algorithms can be parallelised synchronously and asynchronously Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work Explore algorithm benchmark results with tuned hyperparameters Understand how deep RL environments are designedThis guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Editore: Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Lingua: Inglese
Da: Majestic Books, Hounslow, Regno Unito
EUR 72,56
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Editore: Addison-Wesley Professional, 2019
ISBN 10: 0135172381 ISBN 13: 9780135172384
Lingua: Inglese
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 75,34
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