Q( )-learning adaptive fuzzy logic controllers for pursuit–evasion differential games

This paper addresses the problem of tuning the input and the output parameters of a fuzzy logic controller. A novel technique that combines Q( )-learning with function approximation (fuzzy inference system) is proposed. The system learns autonomously without supervision or a priori training data. The proposed technique is applied to three different pursuit–evasion differential games. The proposed technique is compared with the classical control strategy, Q( )-learning only, and the technique proposed by Dai et al. (2005) in which a neural network is used as a function approximation for Q-learning. Computer simulations show the usefulness of the proposed technique. Copyright 2011 John Wiley & Sons, Ltd.