This paper addresses the problem of tuning the input and the output parameters of a fuzzy logic controller. A novel technique that combines Q( )-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( )-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.

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A new fuzzy reinforcement learning algorithm that tunes the input and the output parameters of a fuzzy logic controller is proposed in this paper. The proposed algorithm uses three fuzzy inference systems (FISs); one is used as an actor (fuzzy logic controller, FLC), and the other two FISs are used as critics. The proposed algorithm uses the residual gradient value iteration algorithm described in [4] to tune the input and the output parameters of the actor (FLC) of the learning robot. The proposed algorithm also tunes the input and the output parameters of the critics. The proposed algorithm is called the residual gradient fuzzy actor critics learning (RGFACL) algorithm. The proposed algorithm is used to learn a single pursuit-evasion differential game. Simulation results show that the performance of the proposed RGFACL algorithm outperforms the performance of the fuzzy actor critic learning (FACL) and the Q-learning fuzzy inference system (QLFIS) algorithms proposed in [3] and [7], respectively, in terms of convergence and speed of learning.