UMass Machine Learning and Friends Lunch | Main / Motor Primitive Discovery

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.

Philip Thomas is a Ph.D candidate in the School of Computer Science at the University of Massachusetts Amherst, and is advised by Andrew G. Barto. He is a member of the Autonomous Learning Laboratory and received a MS and BS degree in computer science from Case Western Reserve University. His primary research interest is reinforcement learning.