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Pattern-recognizing stochastic learning automata

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2 Author(s)
Barto, A.G. ; Dept. of Comput. & Inf. Sci., Massachusetts Univ., Amherst, MA, USA ; Anandan, P.

A class of learning tasks is described that combines aspects of learning automation tasks and supervised learning pattern-classification tasks. These tasks are called associative reinforcement learning tasks. An algorithm is presented, called the associative reward-penalty, or AR-P algorithm for which a form of optimal performance is proved. This algorithm simultaneously generalizes a class of stochastic learning automata and a class of supervised learning pattern-classification methods related to the Robbins-Monro stochastic approximation procedure. The relevance of this hybrid algorithm is discussed with respect to the collective behaviour of learning automata and the behaviour of networks of pattern-classifying adaptive elements. Simulation results are presented that illustrate the associative reinforcement learning task and the performance of the AR-P algorithm as compared with that of several existing algorithms.

Published in:

Systems, Man and Cybernetics, IEEE Transactions on  (Volume:SMC-15 ,  Issue: 3 )