  We develop a meta-learning framework for simple regret minimization in
bandits. In this framework, a learning agent interacts with a sequence of
bandit tasks, which are sampled i.i.d.\ from an unknown prior distribution, and
learns its meta-parameters to perform better on future tasks. We propose the
first Bayesian and frequentist meta-learning algorithms for this setting. The
Bayesian algorithm has access to a prior distribution over the meta-parameters
and its meta simple regret over $m$ bandit tasks with horizon $n$ is mere
$\tilde{O}(m / \sqrt{n})$. On the other hand, the meta simple regret of the
frequentist algorithm is $\tilde{O}(\sqrt{m} n + m/ \sqrt{n})$. While its
regret is worse, the frequentist algorithm is more general because it does not
need a prior distribution over the meta-parameters. It can also be analyzed in
more settings. We instantiate our algorithms for several classes of bandit
problems. Our algorithms are general and we complement our theory by evaluating
them empirically in several environments.
