  A continual learning agent should be able to build on top of existing
knowledge to learn on new data quickly while minimizing forgetting. Current
intelligent systems based on neural network function approximators arguably do
the opposite---they are highly prone to forgetting and rarely trained to
facilitate future learning. One reason for this poor behavior is that they
learn from a representation that is not explicitly trained for these two goals.
In this paper, we propose OML, an objective that directly minimizes
catastrophic interference by learning representations that accelerate future
learning and are robust to forgetting under online updates in continual
learning. We show that it is possible to learn naturally sparse representations
that are more effective for online updating. Moreover, our algorithm is
complementary to existing continual learning strategies, such as MER and GEM.
Finally, we demonstrate that a basic online updating strategy on
representations learned by OML is competitive with rehearsal based methods for
continual learning. We release an implementation of our method at
https://github.com/khurramjaved96/mrcl .
