Adaptive Policies for Sequential Sampling under Incomplete Information and a Cost Constraint
Machine Learning
2012-01-20 v1 Machine Learning
Optimization and Control
Abstract
We consider the problem of sequential sampling from a finite number of independent statistical populations to maximize the expected infinite horizon average outcome per period, under a constraint that the expected average sampling cost does not exceed an upper bound. The outcome distributions are not known. We construct a class of consistent adaptive policies, under which the average outcome converges with probability 1 to the true value under complete information for all distributions with finite means. We also compare the rate of convergence for various policies in this class using simulation.
Cite
@article{arxiv.1201.4002,
title = {Adaptive Policies for Sequential Sampling under Incomplete Information and a Cost Constraint},
author = {Apostolos Burnetas and Odysseas Kanavetas},
journal= {arXiv preprint arXiv:1201.4002},
year = {2012}
}