Abstract

We introduce a method for actively learning parameterized skills. Parameterized skills are flexible behaviors that can solve any task drawn from a distribution of parameterized reinforcement learning problems. Approaches to learning such skills have been proposed, but limited attention has been given to identifying which training tasks allow for rapid skill acquisition. We construct a non-parametric Bayesian model of skill performance and derive analytical expressions for a novel acquisition criterion capable of identifying tasks that maximize expected improvement in skill performance. We also introduce a spatiotemporal kernel tailored for nonstationary skill performance models. The proposed method is agnostic to policy and skill representation and scales independently of task dimensionality. We evaluate it on a non-linear simulated catapult control problem over arbitrarily mountainous terrains.