Finding hidden hierarchy in reinforcement learning

Abstract

HEXQ is a reinforcement learning algorithm that decomposes a problem into subtasks and constructs a hierarchy using state variables. The maximum number of levels is constrained by the number of variables representing a state. In HEXQ, values learned for a subtask can be reused in different contexts if the subtasks are identical. If not, values for non-identical subtasks need to be trained separately. This paper introduces a method that tackles these two restrictions. Experimental results show that this method can save the training time dramatically. © Springer-Verlag Berlin Heidelberg 2005.