Provable Hierarchical Lifelong Learning with a Sketch-based Modular Architecture

Understand

We propose a modular architecture for the lifelong learning of hierarchically structured tasks.

• Specifically, we prove that our architecture is theoretically able to learn tasks that can be solved by functions that are learnable given access to functions for other, previously learned tasks as subroutines.
• We empirically show that some tasks that we can learn in this way are not learned by standard training methods in practice; indeed, prior work suggests that some such tasks cannot be learned by any efficient method without the aid of the simpler tasks.
• We also consider methods for identifying the tasks automatically, without relying on explicitly given indicators.