Markov decision processes in artificial intelligence
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Markov decision processes in artificial intelligence MDPs, beyond MDPs and applications by Olivier Sigaud

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Published by ISTE ; Hoboken, NJ : Wiley in London .
Written in English


Book details:

Edition Notes

Includes bibliographical references and index.

Statementedited by Olivier Sigaud, Olivier Buffet
Classifications
LC ClassificationsQ335 .M374 2010
The Physical Object
Paginationxxii, 457 p. :
Number of Pages457
ID Numbers
Open LibraryOL24521276M
ISBN 101848211678
ISBN 109781848211674
LC Control Number2009048651
OCLC/WorldCa441199599

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Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent Cited by: 1. Artificial intelligence--Mathematics. 2. Artificial intelligence--Statistical methods. 3. Markov processes. 4. Statistical decision. I. Sigaud, Olivier. II. Buffet, Olivier. QM 'dc22 British Library Cataloguing-in-Publication Data A CIP record for this book . ‎Markov Decision Processes in Artificial Intelligence on Apple Books ‎Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under . Markov Decision Processes in Artificial Intelligence by Olivier Sigaud, Olivier Buffet Get Markov Decision Processes in Artificial Intelligence now with O’Reilly online learning. O’Reilly members experience live online training, plus books.

Techniques in Artificial Intelligence Markov Decision Processes •Framework •Markov chains •MDPs •Value iteration •Extensions Now we’re going to think about how to do planning in uncertain domains. It’s an extension of decision File Size: KB. A Markov decision process (MDP) relies on the notions of state, describing the current situation of the agent, action affecting the dynamics of the process, and reward, observed for each transition Cited by: 1. A Markov decision process (MDP) relies on the notions of state, describing the current situation of the agent, action affecting the dynamics of the process, and reward, observed for each transition between Cited by: 1. To explain the Markov Decision Process, we use the same environment example of the book “Artificial Intelligence: A Modern Approach (3rd ed.)“. This environment is called Grid World, it is a simple grid .

A Markov decision process (MDP) is a standard formal framework for modeling stochastic planning and sequential decision making under uncertainty in many disciplines, e.g., artificial intelligence. Markov Decision Processes 1 Introduction. This book presents a decision problem type commonly called sequential decision problems under uncertainty. The first feature of such problems resides in . Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when Cited by: When the structure of the Factored Markov Decision Process (FMDP) is completely described, some known algorithms can be applied to find good policies in a quite efficient way (Guestrin et al., ).