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.
|Fuzzy control, Genetic algorithms, Pursuit-evasion game, Q(λ)-learning, Reinforcement learning|
|2009 IEEE International Conference on Systems, Man and Cybernetics, SMC 2009|
|Organisation||Department of Systems and Computer Engineering|
Desouky, S.F. (Sameh F.), & Schwartz, H.M. (2009). A novel technique to design a fuzzy logic controller using Q(λ)-learning and genetic algorithms in the pursuit-evasion game. In Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics (pp. 2609–2615). doi:10.1109/ICSMC.2009.5346114