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 MRCL, an objective to explicitly learn representations that accelerate future learning and are robust to forgetting under online updates in continual learning. The idea is to optimize the representation such that online updates minimize error on all samples with little forgetting. We show that it is possible to learn representations that are more effective for online updating and that sparsity naturally emerges in these representations. Moreover, our method is complementary to existing continual learning strategies, like MER, which can learn more effectively from representations learned by our objective. Finally, we demonstrate that a basic online updating strategy with our learned representation is competitive with rehearsal based methods for continual learning. We release an implementation of our method at https://github.com/khurramjaved96/mrcl .

本文提出了一种称为OML的目标，其直接最小化灾难性干扰，通过学习在持续学习的在线更新过程中加速未来学习和对遗忘有弹性的表示，我们表明可以学习到更有效的在线更新的自然稀疏表示，并且该算法是现有持续学习策略，如MER和GEM的有效补充，最后，我们证明了通过OML学习的基本在线更新策略与基于重演的持续学习方法具有相同的竞争力。

元学习表征用于连续学习