Aspects of using elman neural network for controlling game object movements in simplified game world

Abstract

This paper describes architecture of an artificial intelligence system based on the Elman neural network. Simple training algorithms and neural network models are not able to solve such a complex problem as movements in the conditions of an independent game world environment, so a combination of a base neural network training algorithm and Q-learning agent approach is used as part of a player behavior control model. The paper also includes results of experiments with different values of model and game world characteristics and shows efficiency of the described approach.