Q learning based on self-organizing fuzzy radial basis function network

Authors:
Xuesong Wang;Yuhu Cheng;Wei Sun
Affiliations:
School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, P.R. China;School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, P.R. China;School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

Citing 6
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Abstract

A fuzzy Q learning based on a self-organizing fuzzy radial basis function (FRBF) network is proposed to solve the ‘curse of dimensionality’ problem caused by state space generalization in the paper. A FRBF network is used to represent continuous action and the corresponding Q value. The interpolation technique is adopted to represent the appropriate utility value for the wining local action of every fuzzy rule. Neurons can be organized by the FRBF network itself. The methods of the structure and parameter learning, based on new adding and merging neurons techniques and a gradient descent algorithm, are simple and effective, with a high accuracy and a compact structure. Simulation results on balancing control of inverted pendulum illustrate the performance and applicability of the proposed fuzzy Q learning scheme to real-world problems with continuous states and continuous actions.