Markov decision processes in artificial intelligence MDPs, beyond MDPs and applications
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...
| Otros Autores: | , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
London : Hoboken, N.J. : Wiley,
ISTE
2010.
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| Colección: | Safari tech books online.
Wiley UBCM ebooks. ISTE |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628185206719 |
| Sumario: | 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 illustr |
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| Notas: | First published 2008 in France by Hermes Science/Lavoisier in two volumes entitled: Processus decisionnels de Markov en intelligence artificielle. |
| Descripción Física: | 1 online resource (457 pages) |
| Bibliografía: | Includes bibliographical references and index. |
| ISBN: | 9781118620106 9781118557426 9781299315471 9781118619872 |
