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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(2): 91-95 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.489

Control Policy with Autocorrelated Noise in Reinforcement Learning for Robotics

Paweł Wawrzyński
Abstract—Direct application of reinforcement learning in robotics rises the issue of discontinuity of control signal. Consecutive actions are selected independently on random, which often makes them excessively far from one another. Such control is hardly ever appropriate in robots, it may even lead to their destruction. This paper considers a control policy in which consecutive actions are modified by autocorrelated noise. That policy generally solves the aforementioned problems and it is readily applicable in robots. In the experimental study it is applied to three robotic learning control tasks: Cart-Pole SwingUp, Half-Cheetah, and a walking humanoid.

Index Terms—Machine learning, reinforcement learning, actorcritics, robotics.

Paweł Wawrzyński is with Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland (e-mail: p.wawrzynski@elka.pw.edu.pl).


Cite: Paweł Wawrzyński, "Control Policy with Autocorrelated Noise in Reinforcement Learning for Robotics," International Journal of Machine Learning and Computing vol. 5, no. 2, pp. 91-95, 2015.

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