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• [P91] A Novel Technique to Design a Fuzzy Logic Controller Using Q$(\lambda)$-Learning and Genetic Algorithms in The Pursuit-Evasion Game
  Author(s): Sameh F. Desouky, Howard M. Schwartz
  
  

  Abstract
  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()-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()-learning only, and unsupervised genetic algorithm learning. Computer simulations show the usefulness of the proposed technique.

  
   

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