The BYU Applied Machine Learning Laboratory focuses on increasing the practicality of theoretical machine learning algorithms, especially as applied to autonomous robotic systems. We concentrate on making reinforcement learning techniques applicable to large problems, and on making it fast enough for on-line use. The lab is currently pursuing research projects in areas such as knowledge transfer, skill selection, skill composition, function approximation, multi-agent decomposition, and adversarial multi-agent learning. Q-learning, suitable function approximation such as RBF and back-propagation networks, and induced abstract models are the primary methodologies we use to solve problems. The lab is directed by Dr. Kevin Seppi, and is operated under the Computer Science Department of Brigham Young University. The lab can be contacted by calling (801) 422-8717, or by contacting Dr. Seppi at (801) 422-4619.
The lab is located in room 3350 of the TMCB on BYU campus in Provo, Utah.