  Online meta-learning has recently emerged as a marriage between batch
meta-learning and online learning, for achieving the capability of quick
adaptation on new tasks in a lifelong manner. However, most existing approaches
focus on the restrictive setting where the distribution of the online tasks
remains fixed with known task boundaries. In this work, we relax these
assumptions and propose a novel algorithm for task-agnostic online
meta-learning in non-stationary environments. More specifically, we first
propose two simple but effective detection mechanisms of task switches and
distribution shift based on empirical observations, which serve as a key
building block for more elegant online model updates in our algorithm: the task
switch detection mechanism allows reusing of the best model available for the
current task at hand, and the distribution shift detection mechanism
differentiates the meta model update in order to preserve the knowledge for
in-distribution tasks and quickly learn the new knowledge for
out-of-distribution tasks. In particular, our online meta model updates are
based only on the current data, which eliminates the need of storing previous
data as required in most existing methods. We further show that a sublinear
task-averaged regret can be achieved for our algorithm under mild conditions.
Empirical studies on three different benchmarks clearly demonstrate the
significant advantage of our algorithm over related baseline approaches.
