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Competing neural networks: Finding a strategy for the game of matching pennies

I. Samengo·D. H. Zanette
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Abstract

The ability of a deterministic, plastic system to learn to imitate stochastic behavior is analyzed. Two neural networks -actually, two perceptrons- are put to play a zero-sum game one against the other. The competition, by acting as a kind of mutually supervised learning, drives the networks to produce an approximation to the optimal strategy, that is to say, a random signal.

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I. Samengo, D. H. Zanette. 2000-02-22. Competing neural networks: Finding a strategy for the game of matching pennies. https://doi.org/10.1103/physreve.62.4049

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