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This paper presents a stochastic distributed algorithm for robust learning in networks of asynchronous sampled-data systems characterized by strongly connected directed graphs, where the response map of each sampled-data system has a quadratic structure, and the interactions between systems describe a Nash game. It is assumed that each sampled-data system has an individual resetting clock, as well...
We present a novel algorithm designed to achieve robust convergence to Nash equilibria in non-cooperative games, where players are not required to participate in the game for all time, neither to know the exact mathematical form of their cost function. In this algorithm each player employs stochastic probing dynamics that only require measurements of its own cost function, together with a dynamic...
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