Motor primitive discovery

Philip S. Thomas, Andrew G. Barto

Abstract: We present a method for autonomous on-line discovery of motor primitives for Markov decision processes with high-dimensional continuous action spaces. These biologically-inspired motor primitives require overhead to compute but form a compressed representation of the action set that allows for improved performance on subsequent learning tasks that have similar dynamics.