Analyzing feature generation for value-function approximation

We analyze a simple, Bellman-error-based approach to generating basis functions for valuefunction approximation. We show that it generates orthogonal basis functions that provably tighten approximation error bounds. We also illustrate the use of this approach in the presence of noise on some sample problems.

• Type: Conference
• Year: 2007
• Where: ICML
• Authors: Ronald Parr, Christopher Painter-Wakefield, Lihong Li, Michael L. Littman
• Keywords: Approximation Error Bounds, ICML 2007, Machine Learning, Orthogonal Basis Functions