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.

翻译：对强盗的有效探索是一个基本的在线学习问题。 我们提出了一个汤普森抽样的变种, 以学习如何更好地探索, 因为它与从未知的先前的强盗事件相互作用。 算法的元分解, 因此我们称之为MetATS。 我们提出若干有效的实施MetATS, 并在高山强盗中分析它。 我们的分析显示了元学习的好处, 并且具有更广泛的兴趣, 因为我们从一种新颖的、 先前依赖的贝耶斯人身上 得出了对汤普森取样的遗憾。 我们的理论得到了经验评估的补充, 经验评估表明MetATS迅速适应了未知的以前。