Markov decision processes in artificial intelligence - Info and Reading Options
MDPs, beyond MDPs and applications
By Olivier Sigaud
"Markov decision processes in artificial intelligence" was published by ISTE ; Hoboken, N.J. : Wiley in 2010 - London, it has 480 pages and the language of the book is English.
“Markov decision processes in artificial intelligence” Metadata:
- Title: ➤ Markov decision processes in artificial intelligence
- Author: Olivier Sigaud
- Language: English
- Number of Pages: 480
- Publisher: ISTE ; Hoboken, N.J. : Wiley
- Publish Date: 2010
- Publish Location: London
- Dewey Decimal Classification: 006.301/509233
- Library of Congress Classification: Q335 .M374 2010Q335.M374 2010
“Markov decision processes in artificial intelligence” Subjects and Themes:
- Subjects: Statistical methods - Mathematics - Statistical decision - Artificial intelligence - Markov processes
Edition Specifications:
- Pagination: p. cm.
Edition Identifiers:
- The Open Library ID: OL23971939M - OL18634160W
- Online Computer Library Center (OCLC) ID: 441199599 - 876268806
- Library of Congress Control Number (LCCN): 2009048651
- ISBN-13: 9781848211674 - 9781118620106
- All ISBNs: 9781848211674 - 9781118620106
AI-generated Review of “Markov decision processes in artificial intelligence”:
"Markov decision processes in artificial intelligence" Description:
Open Data:
Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as Reinforcement Learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in Artificial Intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, Reinforcement Learning, Partially Observable MDPs, Markov games and the use of non-classical criteria). Then it presents more advanced research trends in the domain and gives some concrete examples using illustrative applications
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