Header logo is ei

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

2007

Conference Paper

ei


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.

Author(s): Peters, J. and Schaal, S.
Journal: Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007)
Pages: 295-300
Year: 2007
Month: April
Day: 0
Publisher: D-Side

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: 15th European Symposium on Artificial Neural Networks (ESANN 2007)
Event Place: Brugge, Belgium

Address: Evere, Belgium
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
Web

BibTex

@inproceedings{4725,
  title = {Applying the Episodic Natural Actor-Critic
  Architecture to Motor Primitive Learning},
  author = {Peters, J. and Schaal, S.},
  journal = {Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007)},
  pages = {295-300},
  publisher = {D-Side},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Evere, Belgium},
  month = apr,
  year = {2007},
  month_numeric = {4}
}