Min Sub Kim (PhD 2009)

Co-supervised by Will Uther, Min worked on an approach to improving an approximate solution in reinforcement learning by augmenting it with a small overriding patch. Many approximate solutions are smaller and easier to produce than a flat solution, but the best solution within the constraints of the approximation may fall well short of global optimality. Min worked on a technique for efficiently learning a small patch to reduce this gap. After graduating, Min joined the machine learning group at CISRA.