Isbn: 9789811949357 - reinforcement learning: theory and python implementation (7 resultados)

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  • Idioma: Inglés

    Editorial: Springer, 2025

    9811949352 / 9789811949357

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    Librería: Books Puddle, Woodside, NY, Estados Unidos de AmericaBooks Puddle

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    Editorial: Springer, 2025

    9811949352 / 9789811949357

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    Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH

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    Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Reinforcement Learning: Theory and Python Implementation is a tutorial book on reinforcement learning, with explanations of both theory and applications. Starting from a uniform mathematical framework, this book derives the theory of modern reinforcement learning systematically and introduces all mainstream reinforcement learning algorithms such as PPO, SAC, and MuZero. It also covers key technologies of GPT training such as RLHF, IRL, and PbRL. Every chapter is accompanied by high-quality implementations, and all implementations of deep reinforcement learning algorithms are with both TensorFlow and PyTorch. Codes can be found on GitHub along with their results and are runnable on a conventional laptop with either Windows, macOS, or Linux.This book is intended for readers who want to learn reinforcement learning systematically and apply reinforcement learning to practical applications. It is also ideal to academical researchers who seek theoretical foundation or algorithm enhancement in their cutting-edge AI research.…

  • Idioma: Inglés

    Editorial: Springer, Springer Sep 2025, 2025

    9811949352 / 9789811949357

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    Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.

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    EUR 64,19

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    Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Reinforcement Learning: Theory and Python Implementation is a tutorial book on reinforcement learning, with explanations of both theory and applications. Starting from a uniform mathematical framework, this book derives the theory of modern reinforcement learning systematically and introduces all mainstream reinforcement learning algorithms such as PPO, SAC, and MuZero. It also covers key technologies of GPT training such as RLHF, IRL, and PbRL. Every chapter is accompanied by high-quality implementations, and all implementations of deep reinforcement learning algorithms are with both TensorFlow and PyTorch. Codes can be found on GitHub along with their results and are runnable on a conventional laptop with either Windows, macOS, or Linux.This book is intended for readers who want to learn reinforcement learning systematically and apply reinforcement learning to practical applications. It is also ideal to academical researchers who seek theoretical foundation or algorithm enhancement in their cutting-edge AI research. 584 pp. Englisch. …

  • Idioma: Inglés

    Editorial: Springer, 2025

    9811949352 / 9789811949357

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    Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books

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  • Idioma: Inglés

    Editorial: Springer, 2025

    9811949352 / 9789811949357

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    Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios

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    EUR 94,08

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  • Idioma: Inglés

    Editorial: Springer Verlag GmbH, 2025

    9811949352 / 9789811949357

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    Librería: moluna, Greven, Alemaniamoluna

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    EUR 55,78

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    Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

  • Idioma: Inglés

    Editorial: Springer, Springer Sep 2025, 2025

    9811949352 / 9789811949357

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    Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000

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    Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Reinforcement Learning: Theory and Python Implementation is a tutorial book on reinforcement learning, with explanations of both theory and applications. Starting from a uniform mathematical framework, this book derives the theory of modern reinforcement learning systematically and introduces all mainstream reinforcement learning algorithms such as PPO, SAC, and MuZero. It also covers key technologies of GPT training such as RLHF, IRL, and PbRL. Every chapter is accompanied by high-quality implementations, and all implementations of deep reinforcement learning algorithms are with both TensorFlow and PyTorch. Codes can be found on GitHub along with their results and are runnable on a conventional laptop with either Windows, macOS, or Linux.This book is intended for readers who want to learn reinforcement learning systematically and apply reinforcement learning to practical applications. It is also ideal to academical researchers who seek theoretical foundation or algorithm enhancement in their cutting-edge AI research.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 584 pp. Englisch.…