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I am pleased to have this book by Richard Sutton and Andrew Barto as one of the first
books in the new Adaptive Computation and Machine Learning series. This textbook
presents a comprehensive introduction to the exciting field of reinforcement learning.
Written by two of the pioneers in this field, it provides students, practitioners, and
researchers with an intuitive understanding of the central concepts of reinforcement
learning as well as a precise presentation of the underlying mathematics. The book
also communicates the excitement of recent practical applications of reinforcement
learning and the relationship of reinforcement learning to the core questions in artifical
intelligence. Reinforcement learning promises to be an extremely important new
technology with immense practical impact and important scientific insights into the
organization of intelligent systems.
The goal of building systems that can adapt to their environments and learn from
their experience has attracted researchers from many fields, including computer science,
engineering, mathematics, physics, neuroscience, and cognitive science. Out
of this research has come a wide variety of learning techniques that have the potential
to transform many industrial and scientific fields. Recently, several research
communities have begun to converge on a common set of issues surrounding supervised,
unsupervised, and reinforcement learning problems. The MIT Press series
on Adaptive Computation and Machine Learning seeks to unify the many diverse
strands of machine learning research and to foster high quality research and innovative
applications.
Thomas Diettrich
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Mark Lee
2005-01-04