Publication Details

SELECT * FROM publications WHERE Record_Number=11202
Reference TypeConference Proceedings
Author(s)Deramo, C.; Nuara, A.; Restelli, M.
Year2016
TitleEstimating the Maximum Expected Value through Gaussian Approximation
Journal/Conference/Book TitleICML
AbstractThis paper is about the estimation of the maximum expected value of a set of independent random variables. The performance of several learning algorithms (eg, Q-learning) is affected by the accuracy of such estimation. Unfortunately, no unbiased estimator exists. The usual approach of taking the maximum of the sample means leads to large overestimates that may significantly harm the performance of the learning algorithm. Recent works have shown that the cross validation estimator—which is negatively biased—outperforms the maximum estimator in many sequential decision-making scenarios. On the other hand, the relative performance of the two estimators is highly problem-dependent. In this paper, we propose a new estimator for the maximum expected value, based on a weighted average of the sample means, where the weights are computed using Gaussian approximations for the distributions of the sample means. We compare the proposed estimator with the other stateof-the-art methods both theoretically, by deriving upper bounds to the bias and the variance of the estimator, and empirically, by testing the performance on different sequential learning problems.
Link to PDFhttp://proceedings.mlr.press/v48/deramo16.pdf

  

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