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We consider the problem of distributed convergence to efficient outcomes in coordination games through payoff-based learning dynamics, namely aspiration learning. The proposed learning scheme assumes that players reinforce well performed actions, by successively playing these actions, otherwise they randomize among alternative actions. Our first contribution is the characterization of the asymptotic...
This paper presents the stability analysis of the iterative learning control (ILC) for linear discrete-time system when they are subject to output measurement data dropouts. Using the so-called super-vector approach to ILC, the expectation of output error covariance matrix is employed to develop sufficient conditions for monotonic convergence of such an ILC process. The analysis is supported by simulations.
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