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Markov decision processes in artificial intelligence
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...
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Lenguaje: | eng |
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Wiley-ISTE
2013
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Acceso en línea: | http://cds.cern.ch/record/1617136 |
_version_ | 1780932720854040576 |
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author | Sigaud, Olivier Buffet, Olivier |
author_facet | Sigaud, Olivier Buffet, Olivier |
author_sort | Sigaud, Olivier |
collection | CERN |
description | 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 |
id | cern-1617136 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2013 |
publisher | Wiley-ISTE |
record_format | invenio |
spelling | cern-16171362021-04-21T22:03:20Zhttp://cds.cern.ch/record/1617136engSigaud, OlivierBuffet, OlivierMarkov decision processes in artificial intelligenceMathematical Physics and MathematicsMarkov 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 illustrWiley-ISTEoai:cds.cern.ch:16171362013 |
spellingShingle | Mathematical Physics and Mathematics Sigaud, Olivier Buffet, Olivier Markov decision processes in artificial intelligence |
title | Markov decision processes in artificial intelligence |
title_full | Markov decision processes in artificial intelligence |
title_fullStr | Markov decision processes in artificial intelligence |
title_full_unstemmed | Markov decision processes in artificial intelligence |
title_short | Markov decision processes in artificial intelligence |
title_sort | markov decision processes in artificial intelligence |
topic | Mathematical Physics and Mathematics |
url | http://cds.cern.ch/record/1617136 |
work_keys_str_mv | AT sigaudolivier markovdecisionprocessesinartificialintelligence AT buffetolivier markovdecisionprocessesinartificialintelligence |