Performance Comparison of Relational Reinforcement Learning and RBF Neural Networks for Small Mobile Robots

Abstract

A performance of two learning mechanisms for small mobile robots is performed in this paper. Relational reinforcement learning, and radial basis function neural network learned by evolutionary algorithm are trained to perform the same maze exploration task and the results were compared in terms learning speed, accuracy and compactness of the resulting control mechanisms. Advantages of the chosen methods are discussed.