Learning Feature Relevance Through Step Size Adaptation in Temporal-Difference Learning

no code implementations • 8 Mar 2019 • Alex Kearney, Vivek Veeriah, Jaden Travnik, Patrick M. Pilarski, Richard S. Sutton

In this paper, we examine an instance of meta-learning in which feature relevance is learned by adapting step size parameters of stochastic gradient descent---building on a variety of prior work in stochastic approximation, machine learning, and artificial neural networks.

Meta-Learning Representation Learning