  Neural networks suffer from catastrophic forgetting and are unable to
sequentially learn new tasks without guaranteed stationarity in data
distribution. Continual learning could be achieved via replay -- by
concurrently training externally stored old data while learning a new task.
However, replay becomes less effective when each past task is allocated with
less memory. To overcome this difficulty, we supplemented replay mechanics with
meta-learning for rapid knowledge acquisition. By employing a meta-learner,
which \textit{learns a learning rate per parameter per past task}, we found
that base learners produced strong results when less memory was available.
Additionally, our approach inherited several meta-learning advantages for
continual learning: it demonstrated strong robustness to continually learn
under the presence of noises and yielded base learners to higher accuracy in
less updates.
