Cooperativity in Networks of Pattern Recognizing Stochastic Learning Automata

Andrew G. Barto, P. Anandan and Charles W. Anderson
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Andrew G. Barto: University of Massachusetts, Department of Computer and Information Science
P. Anandan: University of Massachusetts, Department of Computer and Information Science
Charles W. Anderson: University of Massachusetts, Department of Computer and Information Science

A chapter in Adaptive and Learning Systems, 1986, pp 235-246 from Springer

Abstract: Abstract A class of learning tasks is described that combines aspects of learning automaton tasks and supervised learning pattern-classification tasks. We call these associative reinforcement learning tasks. An algorithm is presented, called the associative reward-penalty, or A R−P , algorithm, for which a form of optimal performance has been proved. This algorithm simultaneously generalizes a class of stochastic learning automata and a class of supervised learning pattern-classification methods. Simulation results are presented that illustrate the associative reinforcement learning task and the performance of the the A R−P algorithm. Additional simulation results are presented showing how cooperative activity in networks of interconnected A R−P automata can olve difficult nonlinear associative learning problems.

Date: 1986
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DOI: 10.1007/978-1-4757-1895-9_16

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