We consider the problem of efficiently learning optimal control policies and value functions over large state spaces in an online setting in which estimates must be available afte...
Given a set of rectangles we are asked to pack as many of them as possible into a bigger rectangle. The rectangles packed may not overlap and may not be rotated. This problem is NP...
Several reinforcement-learning techniques have already been applied to the Acrobot control problem, using linear function approximators to estimate the value function. In this pape...
This paper considers consensus problems with delayed noisy measurements, and stochastic approximation is used to achieve mean square consensus. For stochastic approximation based c...
Robust tracking of abrupt motion is a challenging task
in computer vision due to the large motion uncertainty. In
this paper, we propose a stochastic approximation Monte
Carlo (...