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

Understand

Modern representation learning methods often struggle to adapt quickly under non-stationarity because they suffer from catastrophic forgetting and decaying plasticity.

• Such problems prevent learners from fast adaptation since they may forget useful features or have difficulty learning new ones.
• Hence, these methods are rendered ineffective for continual learning.
• This paper proposes Utility-based Perturbed Gradient Descent (UPGD), an online learning algorithm well-suited for continual learning agents.