A novel technique to design a fuzzy logic controller using Q(λ)-learning and genetic algorithms in the pursuit-evasion game

This paper presents a novel technique to tune the parameters of a fuzzy logic controller using a combination of reinforcement learning and genetic algorithms. The proposed technique is called a Q(λ)-learning based genetic fuzzy logic controller (QLBGFLC). The proposed technique is applied to a pursuit-evasion game in which the pursuer does not know its control strategy. We assume that we do not even have a simplistic PD controller strategy. The learning goal for the pursuer is to self-learn its control strategy. The pursuer should do that on-line by interaction with the environment; in this case the evader. Our proposed technique is compared with the optimal strategy, Q(λ)-learning only, and unsupervised genetic algorithm learning. Computer simulations show the usefulness of the proposed technique.