Constructing Fuzzy Controllers Based on Reinforcement Learning
Shan-cheng Pei, Shie-Jue Lee · 2007
Traditionally, the fuzzy rules for a fuzzy controller are provided by experts. They cannot be trained from a set of input-output training examples because the correct response of the plant being controlled is delayed and cannot be obtained immediately. In this paper, we propose a novel approach to construct fuzzy rules for a fuzzy controller based on reinforcement learning. A neural network with delays is used to model the evaluation function Q. Fuzzy rules are constructed and added as the learning proceeds. Both the weights of the Q-learning network and the parameters of the fuzzy rules are tuned by gradient descent. Experimental results have shown that the fuzzy rules obtained perform effectively for control.