Decision Making Under Uncertainty and Reinforcement Learning
Theory and Algorithms
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Beschreibung
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.
Presents recent research in decision making under uncertainty, and in particular reinforcement learning and learning with expert advice
Relate the theory to practical problems in reinforcement learning, artificial intelligence and cognitive science
Gives a thorough understanding of statistical decision theory, the meaning of hypothesis testing, automatic methods for designing and interpreting experiments and the relation of statistical decision making to human decision making
Presents recent research in decision making under uncertainty, in particular reinforcement learning Relates the theory to practical problems in reinforcement learning, artificial intelligence, and cognitive science Gives a thorough understanding of statistical decision theory and the meaning of hypothesis testing
Autor*in
Christos Dimitrakakis
Themen in »Decision Making Under Uncertainty and Reinforcement Learning«
Bayesian Inference Cognitive Science Decision Theory Dynamic Programming Game Theory Intelligent Systems Machine Learning Reinforcement Learning