Active learning of parameterized skills

Journal Article

Copyright 2014 by the author(s). 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 non-stationary 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.

Duke Authors

Cited Authors

  • Da Silva, BC; Konidaris, G; Barto, A

Published Date

  • January 1, 2014

Published In

  • 31st International Conference on Machine Learning, ICML 2014

Volume / Issue

  • 5 /

Start / End Page

  • 3736 - 3745

Citation Source

  • Scopus