  Continual learning studies agents that learn from streams of tasks without
forgetting previous ones while adapting to new ones. Two recent
continual-learning scenarios have opened new avenues of research. In
meta-continual learning, the model is pre-trained to minimize catastrophic
forgetting of previous tasks. In continual-meta learning, the aim is to train
agents for faster remembering of previous tasks through adaptation. In their
original formulations, both methods have limitations. We stand on their
shoulders to propose a more general scenario, OSAKA, where an agent must
quickly solve new (out-of-distribution) tasks, while also requiring fast
remembering. We show that current continual learning, meta-learning,
meta-continual learning, and continual-meta learning techniques fail in this
new scenario. We propose Continual-MAML, an online extension of the popular
MAML algorithm as a strong baseline for this scenario. We empirically show that
Continual-MAML is better suited to the new scenario than the aforementioned
methodologies, as well as standard continual learning and meta-learning
approaches.
