Regularized feature selection in reinforcement learning

Abstract

We introduce feature regularization during feature selection for value function approximation. Feature regularization introduces a prior into the selection process, improving function approximation accuracy and reducing overfitting. We show that the smoothness prior is effective in the incremental feature selection setting and present closed-form smoothness regularizers for the Fourier and RBF bases. We present two methods for feature regularization which extend the temporal difference orthogonal matching pursuit (OMP-TD) algorithm and demonstrate the effectiveness of the smoothness prior; smooth Tikhonov OMP-TD and smoothness scaled OMP-TD. We compare these methods against OMP-TD, regularized OMP-TD and least squares TD with random projections, across six benchmark domains using two different types of basis functions.