Q(Lambda)-Learning Fuzzy Logic Controller For A Multi-Robot System

This paper addresses the problem of tuning the input and the output parameters of a fuzzy logic controller. A novel technique that combines Q(lambda)-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 a pursuit-evasion differential game in which both the pursuer and the evader self-learn their control strategies. The proposed technique is compared with the classical control strategy, Q(lambda)-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.