Low-complexity algorithm for restless bandits with imperfect observations

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...

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Bibliographic Details
Published in:	Mathematical methods of operations research (Heidelberg, Germany) Vol. 100; no. 2; pp. 467 - 508
Main Authors:	Liu, Keqin, Weber, Richard, Zhang, Chengzhong
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2024 Springer Nature B.V
Subjects:	Algorithms Business and Management Calculus of Variations and Optimal Control; Optimization Complexity Dynamic programming Markov processes Mathematics Mathematics and Statistics Multi-armed bandit problems Operations Research/Decision Theory Optimization Original Research 93E20 Continuous state space 90B36 93E35 Observation errors Restless bandits Index policy
ISSN:	1432-2994, 1432-5217
Online Access:	Get full text
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