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

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84135

Peters,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Zitation

Peters, J. (2007). Applying the Episodic Natural Actor-Critic Architecture to Motor Primitive Learning. In 15th European Symposium on Artificial Neural Networks (ESANN 2007) (pp. 295-300). Evere, Belgium: D-Side.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-CE15-3
Zusammenfassung
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.