cover

Learning to Play

Reinforcement Learning and Games

Authors: Plaat, Aske

  • Author takes as inspiration breakthroughs in game playing, and using two-agent games to explain the full power of deep reinforcement learning
  • Suitable for advanced undergraduate and graduate courses in artificial intelligence, machine learning, games, and evolutionary computing, and for self-study by professionals
  • Author uses machine learning frameworks such as Gym, TensorFlow, and Keras, and provides exercises to help understand how AI is learning to play
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Buy this book

eBook 53,49 €
price for Spain (gross)
  • The eBook version of this title will be available soon
  • Due: November 17, 2020
  • ISBN 978-3-030-59238-7
  • Digitally watermarked, DRM-free
  • Included format:
  • Immediate eBook download after purchase and usable on all devices
Hardcover 67,59 €
price for Spain (gross)
About this Textbook

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.

About the authors

Prof. Aske Plaat is Professor of Data Science at Leiden University and scientific director of the Leiden Institute of Advanced Computer Science (LIACS). He is co-founder of the Leiden Centre of Data Science (LCDR) and initiated the SAILS stimulation program. His research interests include reinforcement learning, scalable combinatorial reasoning algorithms, games and self-learning systems.

Buy this book

eBook 53,49 €
price for Spain (gross)
  • The eBook version of this title will be available soon
  • Due: November 17, 2020
  • ISBN 978-3-030-59238-7
  • Digitally watermarked, DRM-free
  • Included format:
  • Immediate eBook download after purchase and usable on all devices
Hardcover 67,59 €
price for Spain (gross)
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Bibliographic Information

Bibliographic Information
Book Title
Learning to Play
Book Subtitle
Reinforcement Learning and Games
Authors
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-59238-7
DOI
10.1007/978-3-030-59238-7
Hardcover ISBN
978-3-030-59237-0
Edition Number
1
Number of Pages
X, 334
Number of Illustrations
39 b/w illustrations, 72 illustrations in colour
Topics