Abstract. We present a new reinforcement learning approach for deterministic continuous control problems in environments with unknown, arbitrary reward functions. The difficulty of...
-- The initial resting pose of many industrial parts differs from the orientation desired for assembly. We show that it is possible to align parts during grasping using a standard ...
Empirical divergence maximization is an estimation method similar to empirical risk minimization whereby the Kullback-Leibler divergence is maximized over a class of functions tha...
Critical network management applications increasingly demand fine-grained flow level measurements. However, current flow monitoring solutions are inadequate for many of these appl...
Vyas Sekar, Michael K. Reiter, Walter Willinger, H...
We consider the problem of actively learning the mean values of distributions associated with a finite number of options. The decision maker can select which option to generate t...