A Neural Networks Committee for the Contextual Bandit Problem

Robin Allesiardo; Raphael Feraud; Djallel Bouneffouf

A Neural Networks Committee for the Contextual Bandit Problem

Neural and Evolutionary Computing 2014-09-30 v1 Machine Learning

Authors: Robin Allesiardo , Raphael Feraud , Djallel Bouneffouf

Abstract

This paper presents a new contextual bandit algorithm, NeuralBandit, which does not need hypothesis on stationarity of contexts and rewards. Several neural networks are trained to modelize the value of rewards knowing the context. Two variants, based on multi-experts approach, are proposed to choose online the parameters of multi-layer perceptrons. The proposed algorithms are successfully tested on a large dataset with and without stationarity of rewards.

Keywords

contextual bandit neural networks non-stationary environments multi-expert approach online learning multi-layer perceptrons NeuralBandit

Cite

@article{arxiv.1409.8191,
  title  = {A Neural Networks Committee for the Contextual Bandit Problem},
  author = {Robin Allesiardo and Raphael Feraud and Djallel Bouneffouf},
  journal= {arXiv preprint arXiv:1409.8191},
  year   = {2014}
}

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21st International Conference on Neural Information Processing

A Neural Networks Committee for the Contextual Bandit Problem

Abstract

Keywords

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