Abstract

This paper presents an algorithm for finding approximately optimal policies in very large Markov decision processes by constructing a hierarchical model and then solving it. This strategy sacrifices optimality for the ability to address a large class of very large problems. Our algorithm works efficiently on enumerated-states and factored MDPs by constructing a hierarchical structure that is no larger than both the reduced model of the MDP and the regression tree for the goal in that MDP, and then using that structure to solve for a policy. 1

Citations