We consider reinforcement learning in systems with unknown dynamics. Algorithms such as E3 (Kearns and Singh, 2002) learn near-optimal policies by using "exploration policies...
Two notions of optimality have been explored in previous work on hierarchical reinforcement learning (HRL): hierarchical optimality, or the optimal policy in the space defined by ...
A large number of industrial concurrent programs are being designed based on a model which combines threads with event-based communication. These programs consist of several threa...
Vineet Kahlon, Nishant Sinha, Erik Kruus, Yun Zhan...
We combine a pen and pressure-sensitive tablet input device, and a sketch-based user initialization process, with a general subdivisioncurve Snake to create an intuitive, fast, ac...
We propose a new Bayesian, stochastic tracking algorithm for the segmentation of blood vessels from 3D medical image data. Inspired by the recent developments in particle filterin...
David Lesage, Elsa D. Angelini, Isabelle Bloch, Ga...