A Markov decision process (MDP) in which the state of the system cannot be fully or precisely observed, e.g., only part of the state is known and/or the state observation has some error. In principle, such a model can be converted to a fully observed MDP by introducing an “information” or “belief” state that may be infinite dimensional, corresponding to a probability distribution over the original state.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsEditor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this entry
Cite this entry
(2013). Partially Observed Markov Decision Processes. In: Gass, S.I., Fu, M.C. (eds) Encyclopedia of Operations Research and Management Science. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1153-7_200580
Download citation
DOI: https://doi.org/10.1007/978-1-4419-1153-7_200580
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-1137-7
Online ISBN: 978-1-4419-1153-7
eBook Packages: Business and Economics