  The continual learning problem involves training models with limited capacity
to perform well on a set of an unknown number of sequentially arriving tasks.
While meta-learning shows great potential for reducing interference between old
and new tasks, the current training procedures tend to be either slow or
offline, and sensitive to many hyper-parameters. In this work, we propose
Look-ahead MAML (La-MAML), a fast optimisation-based meta-learning algorithm
for online-continual learning, aided by a small episodic memory. Our proposed
modulation of per-parameter learning rates in our meta-learning update allows
us to draw connections to prior work on hypergradients and meta-descent. This
provides a more flexible and efficient way to mitigate catastrophic forgetting
compared to conventional prior-based methods. La-MAML achieves performance
superior to other replay-based, prior-based and meta-learning based approaches
for continual learning on real-world visual classification benchmarks. Source
code can be found here: https://github.com/montrealrobotics/La-MAML
