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Mean-Field Learning: a Survey
[article]
2012
arXiv
pre-print
In this paper we study iterative procedures for stationary equilibria in games with large number of players. Most of learning algorithms for games with continuous action spaces are limited to strict contraction best reply maps in which the Banach-Picard iteration converges with geometrical convergence rate. When the best reply map is not a contraction, Ishikawa-based learning is proposed. The algorithm is shown to behave well for Lipschitz continuous and pseudo-contractive maps. However, the
arXiv:1210.4657v1
fatcat:63ofgy5vwfdt7j4kcw4za777wq