Model approximation for HEXQ hierarchical reinforcement learning

Abstract

HEXQ is a reinforcement learning algorithm that discovers hierarchical structure automatically. The generated task hierarchy represents the problem at different levels of abstraction. In this paper we extend HEXQ with heuristics that automatically approximate the structure of the task hierarchy. Construction, learning and execution time, as well as storage requirements of a task hierarchy may be significantly reduced and traded off against solution quality. © Springer-Verlag Berlin Heidelberg 2004.