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A reinforcement learning-based architecture for fuzzy logic controlThis paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction 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.
Document ID
19920055502
Acquisition Source
Legacy CDMS
Document Type
Reprint (Version printed in journal)
Authors
Berenji, Hamid R.
(NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
August 15, 2013
Publication Date
February 1, 1992
Publication Information
Publication: International Journal of Approximate Reasoning
Volume: 6
Issue: 2, Fe
ISSN: 0888-613X
Subject Category
Cybernetics
Accession Number
92A38126
Distribution Limits
Public
Copyright
Other

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