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Lack of performance when it comes to continual learning over non-stationarydistributions of data remains a major challenge in scaling neural networklearning to more human realistic settings. In this work we propose a newconceptualization of the continual learning problem in terms of a temporallysymmetric trade-off between transfer and interference that can be optimized byenforcing gradient alignment across examples. We then propose a new algorithm,Meta-Experience Replay (MER), that directly exploits this view by combiningexperience replay with optimization based meta-learning. This method learnsparameters that make interference based on future gradients less likely andtransfer based on future gradients more likely. We conduct experiments acrosscontinual lifelong supervised learning benchmarks and non-stationaryreinforcement learning environments demonstrating that our approachconsistently outperforms recently proposed baselines for continual learning.Our experiments show that the gap between the performance of MER and baselinealgorithms grows both as the environment gets more non-stationary and as thefraction of the total experiences stored gets smaller.

Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference