Basis functions derived from an undirected graph connecting nearby samples from a Markov decision process (MDP) have proven useful for approximating value functions. The success o...
We address the problem of automatically constructing basis functions for linear approximation of the value function of a Markov Decision Process (MDP). Our work builds on results ...
We introduce a new algorithm based on linear programming that approximates the differential value function of an average-cost Markov decision process via a linear combination of p...
The majority of the work in the area of Markov decision processes has focused on expected values of rewards in the objective function and expected costs in the constraints. Althou...
Policy evaluation is a critical step in the approximate solution of large Markov decision processes (MDPs), typically requiring O(|S|3 ) to directly solve the Bellman system of |S...