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doi:10.1016/0888-613X(92)90020-Z    
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Copyright © 1992 Published by Elsevier Science Inc.

A reinforcement learning—based architecture for fuzzy logic control

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Hamid R. BerenjiCorresponding Author Contact Information

Artificial Intelligence Research Branch, NASA Ames Research Center, Moffett Field, California, USA


Available online 20 May 2003.

Abstract

This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjuction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.

Author Keywords: approximate reasoning; fuzzy logic control; neural networks; reinforcement learning; adaptive control

Article Outline

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Corresponding Author Contact InformationCorresponding author. Address correspondence to Hamid R. Berenji, Sterling Software Corp., Artificial Intelligence Research Branch, NASA Ames Research Center, Moffett Field, CA 94035.


 
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