Information Directed Policy Sampling for Partially Observable Markov Decision Processes with Parametric Uncertainty

Peeyush Kumar and Archis Ghate ()
Additional contact information
Peeyush Kumar: University of Washington
Archis Ghate: University of Washington

A chapter in Advances in Service Science, 2019, pp 201-209 from Springer

Abstract: Abstract This paper formulates partially observable Markov decision processes, where state-transition probabilities and measurement outcome probabilities are characterized by unknown parameters. An information theoretic solution method that adaptively manages the resulting exploitation-exploration trade-off is proposed. Numerical experiments for response guided dosing in healthcare are presented.

Date: 2019
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-030-04726-9_20

Ordering information: This item can be ordered from
http://www.springer.com/9783030047269

DOI: 10.1007/978-3-030-04726-9_20

Access Statistics for this chapter

More chapters in Springer Proceedings in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().