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• W2592379963 abstract "This paper considers a class of reinforcement-learning that belongs to the family of Learning Automata and provides a stochastic-stability analysis in strategic-form 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 of the dynamics through stochastic approximations, where the asymptotic behavior can be associated with the limit points of an ordinary-differential equation (ODE). However, analyzing global convergence through the ODE-approximation requires the existence of a Lyapunov or a potential function, which naturally restricts the applicabity of these algorithms to a fine class of games. To overcome these limitations, this paper introduces an alternative framework for analyzing stochastic-stability that is based upon an explicit characterization of the (unique) invariant probability measure of the induced Markov chain." @default.
• W2592379963 created "2017-03-16" @default.
• W2592379963 creator A5013626823 @default.
• W2592379963 date "2017-02-27" @default.
• W2592379963 modified "2023-09-27" @default.
• W2592379963 title "Stochastic Stability Analysis of Perturbed Learning Automata with Constant Step-Size in Strategic-Form Games" @default.
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• W2592379963 hasPublicationYear "2017" @default.
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