In this paper we develop a theoretical analysis of the performance of sampling-based fitted value iteration (FVI) to solve infinite state-space, discounted-reward Markovian decisi...
Reinforcement Learning methods for controlling stochastic processes typically assume a small and discrete action space. While continuous action spaces are quite common in real-wor...
A number of algorithms of clustering spatial data for reducing the number of disk seeks required to process spatial queries have been developed. One of the algorithms is the scheme...
We show how to build hierarchical, reduced-rank representation for large stochastic matrices and use this representation to design an efficient algorithm for computing the largest...
In this paper we present a simple and general new No Free Lunch-like result that applies to revisiting algorithms searching arbitrary problem sets. We begin by unifying the assumpt...