Fast Feature Selection for Linear Value Function Approximation

Book One

Volume

Proceedings of the International Conference on Automated Planning and Scheduling, 29

Planning and Learning

Linear value function approximation is a standard approach to solving reinforcement learning problems with large state spaces. Since designing good approximation features is difficult, automatic feature selection is an important research topic. We propose a new method for feature selection that is based on a low-rank factorization of the transition matrix. Our approach derives features directly from high-dimensional raw inputs, such as image data. The method is easy to implement using SVD, and our experiments show that it is faster and more stable than alternative methods.

10.1609/icaps.v29i1.3527