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Discrete-time average-cost mean-field games on Polish spaces
(TÜBİTAK, 2020)
In stochastic dynamic games, when the number of players is sufficiently large and the interactions between agents depend on empirical state distribution, one way to approximate the original game is to introduce infinite-population ...
Value iteration algorithm for mean-field games
(Elsevier, 2020-09)
In the literature, existence of mean-field equilibria has been established for discrete-time mean field games under both the discounted cost and the average cost optimality criteria. In this paper, we provide a value ...
Markov-Nash equilibria in mean-field games with discounted cost
(Society for Industrial and Applied Mathematics Publications, 2018)
In this paper, we consider discrete-time dynamic games of the mean-field type with a finite number $N$ of agents subject to an infinite-horizon discounted-cost optimality criterion. The state space of each agent is a Polish ...
Approximate markov-nash equilibria for discrete-time risk-sensitive mean-field games
(Informs, 2020-11)
In this paper, we study a class of discrete-time mean-field games under the infinite-horizon risk-sensitive optimality criterion. Risk sensitivity is introduced for each agent (player) via an exponential utility function. ...
Approximate nash equilibria in partially observed stochastic games with mean-field interactions
(Informs, 2019-08)
Establishing the existence of Nash equilibria for partially observed stochastic dynamic games is known to be quite challenging, with the difficulties stemming from the noisy nature of the measurements available to individual ...
Large deviations principle for discrete-time mean-field games
(Springer, 2021-11)
In this paper, we establish a large deviations principle (LDP) for interacting particle systems that arise from state and action dynamics of discrete-time mean-field games under the equilibrium policy of the infinite-population ...
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