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.