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Learning and tuning fuzzy logic controllers through reinforcements

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2 Author(s)
Berenji, H.R. ; NASA Ames Res. Center, Mountain View, CA, USA ; Khedkar, P.

A method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. It is shown that: the generalized approximate-reasoning-based intelligent control (GARIC) architecture learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing

Published in:

Neural Networks, IEEE Transactions on  (Volume:3 ,  Issue: 5 )

Date of Publication:

Sep 1992

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