Abstract: 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. PDF
Keywords: Differential game; function approximation; fuzzy control; pursuit-evasion; Q(λ)- learning; reinforcement learning..