This book presents recent research in decision making under
uncertainty, in particular reinforcement learning and learning with
expert advice. The core elements of decision theory, Markov
decision processes and reinforcement learning have not been
previously collected in a concise volume. Our aim with this book
was to provide a solid theoretical foundation with elementary
proofs of the most important theorems in the field, all collected
in one place, and not typically found in introductory textbooks.
This book is addressed to graduate students that are interested in
statistical decision making under uncertainty and the foundations
of reinforcement learning.
General
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