Q(λ)‐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(λ)‐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(λ)‐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.