Evolutionary trained radial basis function networks for robot control

Abstract

An emergence of intelligent behaviour within a simple robotic agent is studied in this paper. The radial basis function neural network is used as the control mechanism of the robot. Evolutionary algorithm is used to train the agent to perform several tasks. A comparison to multilayer perceptron neural networks and reinforcement learning is made and the results are discussed.