A Double Layered State Space Construction Method for Reinfrocement Learning Agents

摘要

In this paper, we propose a new double-layered state space construction method which consists of Fritzke's Growing Neural Gas algorithm and a class management mechanism of GNG units. The classification algorithm yields a new class by referring to anticipation error, anticipation vectors of an originated class, and anticipation vectors GNG units belonging in the originated class.