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 a fuzzy inference system as a function approximation is proposed. The system learns autonomously without supervision or a priori training data. The proposed technique is applied to two different differential games. The proposed technique is compared with the classical control strategy, Q(λ)-learning only, and the technique proposed in [1] in which a neural network is used as a function approximation for Q-learning. Computer simulations show the usefulness of the proposed technique.

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Keywords Differential game, Function approximation, Fuzzy control, Q(λ)- learning, Reinforcement learning
Persistent URL dx.doi.org/10.1109/ISDA.2010.5687283
Conference 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10
Citation
Desouky, S.F. (Sameh F.), & Schwartz, H.M. (2010). Q(λ)-learning fuzzy logic controller for differential games. In Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10 (pp. 109–114). doi:10.1109/ISDA.2010.5687283