This paper presents a general method to derive tight rates of convergence for numerical approximations in optimal control when we consider variable resolution grids. We study the ...
We present JoSTLe, an algorithm that performs value iteration on control problems with continuous actions, allowing this useful reinforcement learning technique to be applied to p...
Christopher K. Monson, David Wingate, Kevin D. Sep...
Depth from triangulation has traditionally been treated in a number of separate threads in the computer vision literature, with methods like stereo, laser scanning, and coded stru...
James Davis, Ravi Ramamoorthi, Szymon Rusinkiewicz
— Partially Observable Markov Decision Processes (POMDPs) offer a powerful mathematical framework for making optimal action choices in noisy and/or uncertain environments, in par...
Traditionally, Markov models have not been successfully used for compression of signal data other than binary image data. Due to the fact that exact substring matches in non-binar...