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Decision Making Under Uncertainty and Reinforcement Learning = Theory and Algorithms /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Decision Making Under Uncertainty and Reinforcement Learning/ by Christos Dimitrakakis, Ronald Ortner.
Reminder of title:	Theory and Algorithms /
Author:	Dimitrakakis, Christos.
other author:	Ortner, Ronald.
Description:	XIII, 243 p. 67 illus., 62 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
Online resource:	https://doi.org/10.1007/978-3-031-07614-5
ISBN:	9783031076145

Decision Making Under Uncertainty and Reinforcement Learning = Theory and Algorithms /
Dimitrakakis, Christos.

Decision Making Under Uncertainty and Reinforcement LearningTheory and Algorithms /[electronic resource] :by Christos Dimitrakakis, Ronald Ortner. - 1st ed. 2022. - XIII, 243 p. 67 illus., 62 illus. in color.online resource. - Intelligent Systems Reference Library,2231868-4408 ;. - Intelligent Systems Reference Library,67.

Introduction -- Subjective probability and utility -- Decision problems -- Estimation. .

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. .

ISBN: 9783031076145

Standard No.: 10.1007/978-3-031-07614-5doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Decision Making Under Uncertainty and Reinforcement Learning = Theory and Algorithms /
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