  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.
