Low-complexity algorithm for restless bandits with imperfect observations

Keqin Liu (), Richard Weber () and Chengzhong Zhang ()
Additional contact information
Keqin Liu: Xi’an Jiaotong-Liverpool University
Richard Weber: University of Cambridge
Chengzhong Zhang: National Center for Applied Mathematics

Mathematical Methods of Operations Research, 2024, vol. 100, issue 2, No 3, 467-508

Abstract: Abstract We consider a class of restless bandit problems that finds a broad application area in reinforcement learning and stochastic optimization. We consider N independent discrete-time Markov processes, each of which had two possible states: 1 and 0 (‘good’ and ‘bad’). Only if a process is both in state 1 and observed to be so does reward accrue. The aim is to maximize the expected discounted sum of returns over the infinite horizon subject to a constraint that only M $$(

Keywords: Restless bandits; Continuous state space; Observation errors; Index policy; 90B36; 93E20; 93E35 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00186-024-00868-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:mathme:v:100:y:2024:i:2:d:10.1007_s00186-024-00868-x

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/00186

DOI: 10.1007/s00186-024-00868-x

Access Statistics for this article

Mathematical Methods of Operations Research is currently edited by Oliver Stein

More articles in Mathematical Methods of Operations Research from Springer, Gesellschaft für Operations Research (GOR), Nederlands Genootschap voor Besliskunde (NGB)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().