Acceleration of association-rule based Markov decision processes

Abstract

In this paper, we present a new approach for the estimation of Markov decision processes based on efficient association rule mining techniques such as Apriori. For the fastest solution of the resulting association-rule based Markov decision process, several accelerating procedures such as asynchronous updates and prioritization using a static ordering have been applied. A new criterion for state reordering in decreasing order of maximum reward is also compared with a modified topological reordering algorithm. Experimental results obtained on a finite state and action-space stochastic shortest path problem demonstrate the feasibility of the new approach.