Utility-based Perturbed Gradient Descent: An Optimizer for Continual Learning

Modern representation learning methods may fail to adapt quickly under non-stationarity since they suffer from the problem of catastrophic forgetting and decaying plasticity. Such problems prevent learners from fast adaptation to changes since they result in increasing numbers of saturated features and forgetting useful features when presented with new experiences. Hence, these methods are rendered ineffective for continual learning. This paper proposes Utility-based Perturbed Gradient Descent (UPGD), an online representation-learning algorithm well-suited for continual learning agents with no knowledge about task boundaries. UPGD protects useful weights or features from forgetting and perturbs less useful ones based on their utilities. Our empirical results show that UPGD alleviates catastrophic forgetting and decaying plasticity, enabling modern representation learning methods to work in the continual learning setting.