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Learning to play = reinforcement lea...

Plaat, Aske.

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Learning to play = reinforcement learning and games /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Learning to play/ by Aske Plaat.
其他題名:	reinforcement learning and games /
作者:	Plaat, Aske.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xiii, 330 p. :ill., digital ;24 cm.
內容註:	Introduction -- Intelligence and Games -- Reinforcement Learning -- Heuristic Planning -- Adaptive Sampling -- Function Approximation -- Self-Play -- Conclusion -- App. A, Deep Reinforcement Learning Environments -- App. B, Running Python -- App. C, Tutorial for the Game of Go -- App. D, AlphaGo Technical Details -- References -- List of Figures -- List of Tables -- List of Algorithms -- Index.
Contained By:	Springer Nature eBook
標題:	Reinforcement learning. -
電子資源:	https://doi.org/10.1007/978-3-030-59238-7
ISBN:	9783030592387

Learning to play = reinforcement learning and games /
Plaat, Aske.

Learning to playreinforcement learning and games /[electronic resource] :by Aske Plaat. - Cham :Springer International Publishing :2020. - xiii, 330 p. :ill., digital ;24 cm.

Introduction -- Intelligence and Games -- Reinforcement Learning -- Heuristic Planning -- Adaptive Sampling -- Function Approximation -- Self-Play -- Conclusion -- App. A, Deep Reinforcement Learning Environments -- App. B, Running Python -- App. C, Tutorial for the Game of Go -- App. D, AlphaGo Technical Details -- References -- List of Figures -- List of Tables -- List of Algorithms -- Index.

In this textbook the author takes as inspiration recent breakthroughs in game playing to explain how and why deep reinforcement learning works. In particular he shows why two-person games of tactics and strategy fascinate scientists, programmers, and game enthusiasts and unite them in a common goal: to create artificial intelligence (AI) After an introduction to the core concepts, environment, and communities of intelligence and games, the book is organized into chapters on reinforcement learning, heuristic planning, adaptive sampling, function approximation, and self-play. The author takes a hands-on approach throughout, with Python code examples and exercises that help the reader understand how AI learns to play. He also supports the main text with detailed pointers to online machine learning frameworks, technical details for AlphaGo, notes on how to play and program Go and chess, and a comprehensive bibliography. The content is class-tested and suitable for advanced undergraduate and graduate courses on artificial intelligence and games. It's also appropriate for self-study by professionals engaged with applications of machine learning and with games development. Finally it's valuable for any reader engaged with the philosophical implications of artificial and general intelligence, games represent a modern Turing test of the power and limitations of AI.

ISBN: 9783030592387

Standard No.: 10.1007/978-3-030-59238-7doiSubjects--Topical Terms:

1006373
Reinforcement learning.

LC Class. No.: Q325.6 / .P53 2020

Dewey Class. No.: 006.31

Learning to play = reinforcement learning and games /
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505 0 $a Introduction -- Intelligence and Games -- Reinforcement Learning -- Heuristic Planning -- Adaptive Sampling -- Function Approximation -- Self-Play -- Conclusion -- App. A, Deep Reinforcement Learning Environments -- App. B, Running Python -- App. C, Tutorial for the Game of Go -- App. D, AlphaGo Technical Details -- References -- List of Figures -- List of Tables -- List of Algorithms -- Index.
520 $a In this textbook the author takes as inspiration recent breakthroughs in game playing to explain how and why deep reinforcement learning works. In particular he shows why two-person games of tactics and strategy fascinate scientists, programmers, and game enthusiasts and unite them in a common goal: to create artificial intelligence (AI) After an introduction to the core concepts, environment, and communities of intelligence and games, the book is organized into chapters on reinforcement learning, heuristic planning, adaptive sampling, function approximation, and self-play. The author takes a hands-on approach throughout, with Python code examples and exercises that help the reader understand how AI learns to play. He also supports the main text with detailed pointers to online machine learning frameworks, technical details for AlphaGo, notes on how to play and program Go and chess, and a comprehensive bibliography. The content is class-tested and suitable for advanced undergraduate and graduate courses on artificial intelligence and games. It's also appropriate for self-study by professionals engaged with applications of machine learning and with games development. Finally it's valuable for any reader engaged with the philosophical implications of artificial and general intelligence, games represent a modern Turing test of the power and limitations of AI.
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