This paper considers a class of discrete-time reinforcement-learning dynamics and provides a stochasticstability analysis in repeatedly played positive-utility (strategicform) games. For this class of dynamics, convergence to pure Nash equilibria has been demonstrated only for the fine class of potential games. Prior work primarily provides convergence properties through stochastic approximatio...

the concept of ${mathscr{f}}_{st}$-fundamentality is introduced in uniform spaces, generated by some filter ${mathscr{f}}$. its equivalence to the concept of ${mathscr{f}}$-convergence in uniform spaces is proved. this convergence generalizes many kinds of convergence, including the well-known statistical convergence.

In order to solve the traditional network optimization problem, the dual gradient descent algorithm is adopted. Although the algorithm can be realized by a distributed method, its convergence rate is slow. Acceleration dual decline algorithm mainly uses the distributed calculation of the approximative Newton step to improve its convergence rate. Due to the uncertainty characteristic of communic...

in this paper, economic convergence in selected oic (36 countries) is studied. the convergence hypothesis is tested through three approaches: beta convergence, sigma convergence, and time series model. ols is used for estimating the cross section model and cross variance is used to test the distributive model. for analyzing time series model, we used augmented dicky fuller test. beta convergenc...

Injecting weight noise during training has been proposed for almost two decades as a simple technique to improve fault tolerance and generalization of a multilayer perceptron (MLP). However, little has been done regarding their convergence behaviors. Therefore, we presents in this paper the convergence proofs of two of these algorithms for MLPs. One is based on combining injecting multiplicativ...