A double layered state space construction method for reinforcement learning agents

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.