Self-learning 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 The system learns autonomously without supervision or a priori training data Two novel techniques are proposed The first technique combines Q(lambda)-learning with function approximation (fuzzy inference system) to tune the parameters of a fuzzy logic controller operating in continuous state and action spaces The second technique combines Q(lambda)-learning with genetic algorithms to tune the parameters of a fuzzy logic controller in the discrete state and action spaces The proposed techniques are applied to different pursuit-evasion differential games The proposed techniques are compared with the classical control strategy Q(lambda)-learning only reward-based genetic algorithms learning and with the technique proposed by Dai et al (2005) 119] in which a neural network is used as a function approximation for Q-learning Computer simulations show the usefulness of the proposed techniques (C) 2010 Elsevier B V All rights reserved