NDLI: Q-value based genetic reinforcement learning for fuzzy controller design

Content Provider	IEEE Xplore Digital Library
Author	Chia-Feng Juang
Copyright Year	2003
Description	Author affiliation: Dept. of Electr. Eng., Nat. Chung Hsing Univ., Taichung, Taiwan (Chia-Feng Juang)
Abstract	This paper proposes a Q-value based Genetic Reinforcement (QGR) learning scheme for Fuzzy controller design (QGRF). The QGRF fulfills GA-based fuzzy controller design under reinforcement learning environment where only weak reinforcement signals are available. For a fuzzy controller, the precondition part is assigned a priori, and the consequent part is designed by QGRF. In QGRF, each individual in the GA population encodes the consequent part parameters of a fuzzy controller and is associated with a Q-value, which is used as a fitness value for GA evolution. At each time step, an individual is selected according to the Q-values, and then a corresponding fuzzy controller is built and applied to the environment with a critic received. With this critic, Q-learning with eligibility trace is executed. After each trial, GA is performed to search for better consequent parameters based on the learned Q-values. Thus, in QGRF, evolution is performed immediately after the end of one trial in contrast to general GA where many trials are performed before evolution. The feasibility of QGRF is demonstrated through simulations in cart-pole balancing problem with only binary reinforcement signals.
Sponsorship	IEEE IEEE Neurla Networks Soc
Starting Page	185
Ending Page	189
File Size	395682
Page Count	5
File Format	PDF
ISBN	0780378105
DOI	10.1109/FUZZ.2003.1209359
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-05-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetics Fuzzy control Fuzzy systems Inference algorithms Education Signal design Automatic control Design methodology Supervised learning Training data
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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