FUZZY INFERENCE SYSTEMS OPTIMIZATION BY REINFORCEMENT LEARNING
Résumé
Fuzzy rules for control can be effectively tuned via reinforcement learning. Reinforcement learning is a weak learning
method wich only requires information on the succes or failure of the control application. In this paper a reinforcement
learning method is used to tune on line the conclusion part of fuzzy inference system rules. The fuzzy rules are tuned in
order to maximize the return function . To illustrate its effectivness, the learning method is applied to the well known
Cart-Pole balancing system problem. The results obtained show significant improvements of the speed of learning.
Références
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Temporal Back Propagation, IEEE Transactions on
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fuzzy dynamic programming, Proceedings of IEEE
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D. P. Bertsekas, Distributed Dynamic Programming,
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A. G. Barto, R. S. Sutton and C. W. Anderson,
Neuronlike adaptive elements that can solve difficult
learning control problems, IEEE Transactions on
Systems, Man and Cybernetics, SMC-13(05), 1983.
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Thesis, University of Cambridge, England, 1989.
K. Passino, S. Yurkovich, Fuzzy Control, Addison
Wesley, California, 1998.
learning, MIT Press/Bradford Books, Cambridge, MA,
1998.
V. Gullapalli, Reinforcement learning and its appication
to control, Ph. D. Thesis, University of Massachusetts,
Amherst, MA, USA, 1992.
L. P. Kaelbling, M. L. Littman, A. W. Moore,
Reinforcement learning: a survey, Journal of Journal
Artificial Intelligence Research 4, 1996.
J. R. Jang, Self-Learning Fuzzy Controllers Based on
Temporal Back Propagation, IEEE Transactions on
Neural Networks, Vol. 3 No. 5, September 1992.
M. G. Cooper, J. J. Vidal, Genetic Design of Fuzzy
Controller, Proceedings of Second Inernational
Conference on Fuzzy Theory and Technology; Durham,
NC, October, 1993.
A. Bonarini, Evolutionary learning of fuzzy
rules:competition and cooperation, in Fuzzy modeling :
paradigms and practice, Kluwer Academic Publishers,
Norwell, MA, 1995.
H. R. Berenji P. Khedkar, Learning and Tuning Fuzzy
Logic Controllers Through Reinforcement, IEEE
Transactions on Neural Networks, Vol. 3 No. 5,
September 1992.
M. V. Buijtenen, G. Schram, R. Babuska, B.
Verbruggen, Adaptive Fuzzy Control of Satellite
Attitude by Reinforcement Learning, IEEE Transactions
on Fuzzy Systems, Vol. 6, No. 2, May 1998.
H. R. Berenji, Fuzzy Q-Learning: a new approach for
fuzzy dynamic programming, Proceedings of IEEE
international conference on Fuzzy Systems, Nj, 1994.
P. Y. Glorennec, L. Jouffe, Fuzzy Q-Learning,
Procedings of FUZZ-IEEE’97, Barcelona, Spain, July
1997.
P. Y. Glorennec, Reinforcement Learning: an Overview,
ESIT 2000, Aachen, Germany, 14-15 September 2000.
D. P. Bertsekas, Distributed Dynamic Programming,
IEEE transactions on Automatic Control, 27, 1982.
A. G. Barto, R. S. Sutton and C. W. Anderson,
Neuronlike adaptive elements that can solve difficult
learning control problems, IEEE Transactions on
Systems, Man and Cybernetics, SMC-13(05), 1983.
C. Watkins Learning from Delayed Rewards, PhD.
Thesis, University of Cambridge, England, 1989.
K. Passino, S. Yurkovich, Fuzzy Control, Addison
Wesley, California, 1998.
Comment citer
BOUMEHRAZ, Mohamed et al.
FUZZY INFERENCE SYSTEMS OPTIMIZATION BY REINFORCEMENT LEARNING.
Courrier du Savoir, [S.l.], v. 1, avr. 2014.
ISSN 1112-3338.
Disponible à l'adresse : >http://univ-biskra.dz/revues/index.php/cds/article/view/186>. Date de consultation : 22 déc. 2024
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