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Learning to Play - by  Aske Plaat (Hardcover) - 1 of 1

Learning to Play - by Aske Plaat (Hardcover)

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Highlights

  • In this textbook the author takes as inspiration recent breakthroughs in game playing to explain how and why deep reinforcement learning works.
  • About the Author: Prof. Aske Plaat is Professor of Data Science at Leiden University and scientific director of the Leiden Institute of Advanced Computer Science (LIACS).
  • 330 Pages
  • Computers + Internet, Programming

Description



Book Synopsis



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.



From the Back Cover



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 Author



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.
Dimensions (Overall): 9.21 Inches (H) x 6.14 Inches (W) x .81 Inches (D)
Weight: 1.45 Pounds
Suggested Age: 22 Years and Up
Number of Pages: 330
Genre: Computers + Internet
Sub-Genre: Programming
Publisher: Springer
Format: Hardcover
Author: Aske Plaat
Language: English
Street Date: November 22, 2020
TCIN: 85228329
UPC: 9783030592370
Item Number (DPCI): 247-66-1217
Origin: Made in the USA or Imported
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Shipping details

Estimated ship dimensions: 0.81 inches length x 6.14 inches width x 9.21 inches height
Estimated ship weight: 1.45 pounds
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