Decision Making Under Uncertainty and Reinforcement Learning
Theory and Algorithms
(Sprache: Englisch)
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...
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Klappentext zu „Decision Making Under Uncertainty and Reinforcement Learning “
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 inintroductory textbooks. This book is addressed to graduate students that are interested in statistical decision making under uncertainty and the foundations of reinforcement learning.
Inhaltsverzeichnis zu „Decision Making Under Uncertainty and Reinforcement Learning “
Introduction.- Subjective probability and utility.- Decision problems.- Estimation.
Bibliographische Angaben
- Autoren: Christos Dimitrakakis , Ronald Ortner
- 2022, 1st ed. 2022, XIII, 243 Seiten, 62 farbige Abbildungen, Maße: 15,5 x 23,5 cm, Gebunden, Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3031076125
- ISBN-13: 9783031076121
Sprache:
Englisch
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