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• [P1246] Implementation of Fuzzy Q-Learning Based on Modular Fuzzy Model and Parallel Structured Learning
  Author(s): Toshihiko Watanabe
  
  

  Abstract
  In order to realize intelligent agent such as autonomous mobile robots, Reinforcement Learning is one of the necessary techniques in control system. Fuzzy Q-learning is one of the promising approaches for implementation of reinforcement learning function owing to its high ability of model representation. However, in applying fuzzy Q-learning to actual application, the number of iterations for learning also becomes huge as well as almost all Q-learning application. Furthermore convergence performance is often deteriorated owing to its complicated model structure. In this study, implementation method of fuzzy Q-learning is discussed in order to improve the learning performance of fuzzy Q-learning. The modular fuzzy model construction method based on fuzzy Q-learning is proposed in this paper. Multi-grain configuration of modular fuzzy model is compared with parallel structured learning scheme. Through numerical experiments of mountain car task and Acrobot task, I found that the proposed construction of modular fuzzy model improved the performance of fuzzy Q-learning.

  
   

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