Q(λ)-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(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 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(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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