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