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Deep reinforcement learning has attracted considerable attention
recently. Impressive results have been achieved in such diverse
fields as autonomous driving, game playing, molecular
recombination, and robotics. In all these fields, computer programs
have taught themselves to understand problems that were previously
considered to be very difficult. In the game of Go, the program
AlphaGo has even learned to outmatch three of the world's leading
players.Deep reinforcement learning takes its inspiration from the
fields of biology and psychology. Biology has inspired the creation
of artificial neural networks and deep learning, while psychology
studies how animals and humans learn, and how subjects' desired
behavior can be reinforced with positive and negative stimuli. When
we see how reinforcement learning teaches a simulated robot to
walk, we are reminded of how children learn, through playful
exploration. Techniques that are inspired by biology and psychology
work amazingly well in computers: animal behavior and the structure
of the brain as new blueprints for science and engineering. In
fact, computers truly seem to possess aspects of human behavior; as
such, this field goes to the heart of the dream of artificial
intelligence. These research advances have not gone unnoticed by
educators. Many universities have begun offering courses on the
subject of deep reinforcement learning. The aim of this book is to
provide an overview of the field, at the proper level of detail for
a graduate course in artificial intelligence. It covers the
complete field, from the basic algorithms of Deep Q-learning, to
advanced topics such as multi-agent reinforcement learning and meta
learning.
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Computers and Games - 8th International Conference, CG 2013, Yokohama, Japan, August 13-15, 2013, Revised Selected Papers (Paperback, 2014 ed.)
H. Jaap van den Herik, Hiroyuki Iida, Aske Plaat
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R2,368
Discovery Miles 23 680
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Ships in 10 - 15 working days
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This book constitutes the thoroughly refereed post-conference
proceedings of the 8th International Conference on Computers and
Games, CG 2013, held in Yokohama, Japan, in August 2013, in
conjunction with the 17th Computer and Games Tournament and the
20th World Computer-Chess Championship. The 21 papers presented
were carefully reviewed and selected for inclusion in this book.
They cover a wide range of topics which are grouped into five
classes: Monte Carlo Tree Search and its enhancements; solving and
searching; analysis of game characteristic; new approaches; and
serious games.
This book constitutes the thoroughly refereed post-conference
proceedings of the 13th Advances in Computer Games Conference, ACG
2011, held in Tilburg, The Netherlands, in November 2011. The 29
revised full papers presented were carefully reviewed and selected
from numerous submissions. The papers cover a wide range of topics
such as Monte-Carlo tree search and its enhancement, temporal
difference learning, optimization, solving and searching, analysis
of a game characteristic, new approaches, and serious games.
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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Computers and Games - 9th International Conference, CG 2016, Leiden, The Netherlands, June 29 - July 1, 2016, Revised Selected Papers (Paperback, 1st ed. 2016)
Aske Plaat, Walter Kosters, Jaap van den Herik
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R1,557
Discovery Miles 15 570
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Ships in 10 - 15 working days
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This book constitutes the thoroughly refereed post-conference
proceedings of the 9th International Conference on Computers and
Games, CG 2016, held in Leiden, The Netherlands,in conjunction with
the 19th Computer Olympiad and the 22nd World Computer-Chess
Championship. The 20 papers presented were carefully reviewed and
selected of 30 submitted papers. The 20 papers cover a wide range
of computer games and many different research topics in four main
classes which determined the order of publication: Monte Carlo Tree
Search (MCTS) and its enhancements (seven papers), concrete games
(seven papers), theoretical aspects and complexity (five papers)
and cognition model (one paper). The paper Using Partial Tablebases
in Breakthrough by Andrew Isaac and Richard Lorentz received the
Best Paper Award.
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Advances in Computer Games - 14th International Conference, ACG 2015, Leiden, The Netherlands, July 1-3, 2015, Revised Selected Papers (Paperback, 1st ed. 2015)
Aske Plaat, Jaap van den Herik, Walter Kosters
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R2,380
Discovery Miles 23 800
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Ships in 10 - 15 working days
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This book constitutes the thoroughly refereed post-conference
proceedings of the 14th International Conference on Advances in
Computer Games, ACG 2015, held in Leiden, The Netherlands, in July
2015. The 22 revised full papers presented were carefully reviewed
and selected from 34 submissions. The papers cover a wide range of
topics such as Monte-Carlo Tree Search and its enhancements;
theoretical aspects and complexity; analysis of game
characteristics; search algorithms; and machine learning.
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