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 mult-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.