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

Thu Jul 22 09:00 PM -- 11:00 PM (PDT) @

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.

Author Information

Branislav Kveton (Google Research)
Mikhail Konobeev (University of Alberta)
Manzil Zaheer (Google Research)
Chih-wei Hsu (Google Research)
Martin Mladenov (Google)
Craig Boutilier (Google)
Csaba Szepesvari (DeepMind/University of Alberta)

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