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Learning to Play - Reinforcement Learning and Games (Hardcover, 1st ed. 2020)
Loot Price: R2,033
Discovery Miles 20 330
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Learning to Play - Reinforcement Learning and Games (Hardcover, 1st ed. 2020)
Expected to ship within 12 - 17 working days
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Total price: R2,043
Discovery Miles: 20 430
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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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