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Cooperativity in Networks of Pattern Recognizing Stochastic Learning Automata

In: Adaptive and Learning Systems

Author

Listed:
  • 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)

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.

Suggested Citation

  • Andrew G. Barto & P. Anandan & Charles W. Anderson, 1986. "Cooperativity in Networks of Pattern Recognizing Stochastic Learning Automata," Springer Books, in: Kumpati S. Narendra (ed.), Adaptive and Learning Systems, pages 235-246, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4757-1895-9_16
    DOI: 10.1007/978-1-4757-1895-9_16
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