English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

Applying the Episodic Natural Actor-Critic Architecture to Motor Primitive Learning

MPS-Authors
There are no MPG-Authors in the publication available
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Peters, J., & Schaal, S. (2007). Applying the Episodic Natural Actor-Critic Architecture to Motor Primitive Learning. In M. Verleysen (Ed.), Advances in computational intelligence and learning: 15th European Symposium on Artificial Neural Networks: ESANN 2007 (pp. 295-300). Evere, Belgium: D-Side.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-CE15-3
Abstract
In this paper, we investigate motor primitive learning with the Natural Actor-Critic approach. The Natural Actor-Critic consists out of actor updates which are achieved using natural stochastic policy gradients while the critic obtains the natural policy gradient by linear regression. We show that this architecture can be used to learn the building blocks of movement generation, called motor primitives. Motor primitives are parameterized control policies such as splines or nonlinear differential equations with desired attractor properties. We show that our most modern algorithm, the Episodic Natural Actor-Critic outperforms previous algorithms by at least an order of magnitude. We demonstrate the efficiency of this reinforcement learning method in the application of learning to hit a baseball with an anthropomorphic robot arm.