Control Policy with Autocorrelated Noise in Reinforcement Learning for Robotics

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.