The Evolution of Multi-Layer Neural Networks for the Control of Xpilot Agents

Abstract

Learning controllers for the space combat game Xpilot is a difficult problem. Using evolutionary computation to evolve the weights for a neural network could create an effective/adaptive controller that does not require extensive programmer input. Previous attempts have been successful in that the controlled agents were transformed from aimless wanderers into interactive agents, but these methods have not resulted in controllers that are competitive with those learned using other methods. In this paper, we present a neural network learning method that uses a genetic algorithm to select the network inputs and node thresholds, along with connection weights, to evolve competitive Xpilot agents