Simulation-based algorithms for Markov decision processes - Info and Reading Options
By Hyeong Soo Chang
"Simulation-based algorithms for Markov decision processes" was published by Springer in 2007 - London, the book is classified in bibliography genre, it has 189 pages and the language of the book is English.
“Simulation-based algorithms for Markov decision processes” Metadata:
- Title: ➤ Simulation-based algorithms for Markov decision processes
- Author: Hyeong Soo Chang
- Language: English
- Number of Pages: 189
- Publisher: Springer
- Publish Date: 2007
- Publish Location: London
- Genres: bibliography
- Dewey Decimal Classification: 658.4033
- Library of Congress Classification: HD30.23 .S564 2007HF4999.2-6182
“Simulation-based algorithms for Markov decision processes” Subjects and Themes:
- Subjects: ➤ Mathematical models - Decision making - Markov processes - Decision making, mathematical models - Decision Support Techniques - Markov Chains
Edition Specifications:
- Number of Pages: xvii, 189 p. : ill. ; 24 cm.
- Pagination: xvii, 189 p. :
Edition Identifiers:
- The Open Library ID: OL17907904M - OL16930233W
- Online Computer Library Center (OCLC) ID: 74968980
- Library of Congress Control Number (LCCN): 2007920840 - ^^2007920840
- ISBN-13: 9781846286896 - 9781846286902
- ISBN-10: 1846286891 - 1846286905
- All ISBNs: 1846286891 - 1846286905 - 9781846286896 - 9781846286902
AI-generated Review of “Simulation-based algorithms for Markov decision processes”:
"Simulation-based algorithms for Markov decision processes" Table Of Contents:
- 1- 1. Markov decision processes
- 2- 2. Multi
- 3- tage adaptive sampling algorithms
- 4- 3. Population
- 5- ased evolutionary approaches
- 6- 4. Model reference adaptive search
- 7- 5. On
- 8- ine control methods via simulation.
"Simulation-based algorithms for Markov decision processes" Description:
Harvard Library:
"Simulation-based Algorithms for Markov Decision Processes brings, state-of-the-art research together for the first time and presents it in a manner that makes it accessible to researchers with varying interests and backgrounds. In addition to providing numerous specific algorithms, the exposition includes both illustrative numerical examples and rigorous theoretical convergence results. The algorithms developed and analyzed differ from the successful computational methods for solving MDPs based on neuro dynamic programming or reinforcement learning and will complement work in those areas. Furthermore, the authors show how to combine the various algorithms introduced with approximate dynamic programming methods that reduce the size of the state space and ameliorate the effects of dimensionality." "The self-contained approach of this book will appeal not only to researchers in MDPs, stochastic modeling and control, and simulation but will be a valuable source of instruction and reference for students of control and operations research."--BOOK JACKET.
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- Harvard University Library: Location: Widener Library, Harvard University - Shelf Numbers: HD30.23 .S564 2007
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