A Study on Multi-Dimensional Fuzzy Q-learning for Intelligent Robots

Abstract

Reinforcement learning is one of the most impor-tant learning methods for intelligent robots working in unknown/uncertain environments. Multi-dimensional fuzzy Q-learning, an extension of the Q-learning method, has been proposed in this study. The proposed method has been applied for an intelligent robot working in a dynamic environment. The rewards from the evaluation functions and the fuzzy Q-values generated by the neural networks (fuzzy Q-net) are expressed in vector forms in order to obtain optimal behaviors for several different purposes. By applying this learning method, evalua-tion and learning of fuzzy Q-values for the other behaviors can be carried out simultaneously in one trial. We express fuzzy states as the vector of fuzzy sets for input variables of the fuzzy Q-net. The be-havior selection algorithm is also proposed in this study. The simulation results show the effectives of the proposed methods for a mobile robot selects op-timal behavior in a dynamic environment.