Networks of Learning Automata: Techniques for Online Stochastic
Optimization is a comprehensive account of learning automata models
with emphasis on multiautomata systems. It considers synthesis of
complex learning structures from simple building blocks and uses
stochastic algorithms for refining probabilities of selecting
actions. Mathematical analysis of the behavior of games and
feedforward networks is provided. Algorithms considered here can be
used for online optimization of systems based on noisy measurements
of performance index. Also, algorithms that assure convergence to
the global optimum are presented. Parallel operation of automata
systems for improving speed of convergence is described. The
authors also include extensive discussion of how learning automata
solutions can be constructed in a variety of applications.
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