Comparison of RBF network learning and reinforcement learning on the maze exploration problem

Abstract

An emergence of intelligent behavior within a simple robotic agent is studied in this paper. Two control mechanisms for an agent are considered - a radial basis function neural network trained by evolutionary algorithm, and a traditional reinforcement learning algorithm over a finite agent state space. A comparison of these two approaches is presented on the maze exploration problem. © Springer-Verlag Berlin Heidelberg 2008.