Name: Learning to Play: Reinforcement Learning and Games
Brand: Springer Verlag
SKU: 9783030592400
Price: 3486.0 INR
Availability: InStock

Learning to Play: Reinforcement Learning and Games

by Plaat

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ISBN13: 9783030592400
Binding: Paperback
Subject: Computer Science and Information Technology
Publisher: Springer Verlag
Publisher Imprint: Springer
Publication Date: January 1, 2020
Pages: 330
Original Price: EUR 49.99
Language: English
Edition: N/A
Item Weight: 486 grams
BISAC Subject(s): Artificial Intelligence / General, Programming / Games, and Popular Culture

About The Book

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.

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