Eager and Memory-Based Non-Parametric Stochastic Search Methods for Learning Control

Direct policy search has shown to be a successful method to optimize robot controller parameters. However, defining a good parametric form for the controller can be challenging for complex problems. Non-parametric methods provide a flexible alternative and are thus a promising tool in robot skill learning. In this paper, we investigate two nonparametric methods based on similar principles but utilizing differing computing schedules: an eager learner and a memory-based learner. We compare the methods experimentally on two different control problems. Furthermore, we define and evaluate a new 'hybrid' controller that combines the strong points of both of these methods.