An optimal sensor morphology improves adaptability of neural network controllers

Abstract

Animals show an abundance of different sensor morphologies, for example in insect compound eyes. However, the advantages of having highly specific sensor morphologies still remain unclear. In this paper we show that an appropriate sensor morphology can improve the learning performance of an agent's neural controller significantly. Using a sensor morphology that is " optimised" for a given task environment the agent is able to learn faster and to adapt more quickly to changes. © Springer-Verlag Berlin Heidelberg 2002.