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arXiv · 2102.06129

Meta-Thompson Sampling

Branislav Kveton·Mikhail Konobeev·Manzil Zaheer·Chih-wei Hsu·Martin Mladenov·Craig Boutilier·Csaba Szepesvari
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Abstract

Efficient exploration in bandits is a fundamental online learning problem. We propose a variant of Thompson sampling that learns to explore better as it interacts with bandit instances drawn from an unknown prior. The algorithm meta-learns the prior and thus we call it MetaTS. We propose several efficient implementations of MetaTS and analyze it in Gaussian bandits. Our analysis shows the benefit of meta-learning and is of a broader interest, because we derive a novel prior-dependent Bayes regret bound for Thompson sampling. Our theory is complemented by empirical evaluation, which shows that MetaTS quickly adapts to the unknown prior.

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Branislav Kveton, Mikhail Konobeev, Manzil Zaheer, Chih-wei Hsu, Martin Mladenov, Craig Boutilier, Csaba Szepesvari. 2021-02-11. Meta-Thompson Sampling. https://arxiv.org/abs/2102.06129

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