Online Meta-Learning in Adversarial Multi-Armed Bandits. (arXiv:2205.15921v2 [cs.LG] UPDATED)

We study meta-learning for adversarial multi-armed bandits. We consider the
online-within-online setup, in which a player (learner) encounters a sequence
of multi-armed bandit episodes. The player's performance is measured as regret
against the best arm in each episode, according to the losses generated by an
adversary. The difficulty of the problem depends on the empirical distribution
of the per-episode best arm chosen by the adversary. We present an algorithm
that can leverage the non-uniformity in this empirical distribution, and derive …

arxiv learning lg meta meta-learning multi-armed bandits