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Learning in anonymous nonatomic games with applications to first-order mean field games
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We introduce a model of anonymous games with the player dependent action sets. We propose several learning procedures based on the well-known Fictitious Play and Online Mirror Descent and prove their convergence to equilibrium under the classical monotonicity condition. Typical examples are first-order mean field games.
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Cited by 2 Pith papers
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Kullback-Leibler Mirror-Prox for Measure-Valued Variational Inequalities and Mean-Field Equilibria
A relative-entropy extragradient algorithm provably computes static mean field equilibria, with explicit trade-offs between grid fineness and iteration count.
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Population-aware Online Mirror Descent for Mean-Field Games with Common Noise by Deep Reinforcement Learning
Master OMD is a population-aware online mirror descent algorithm that learns master policies for finite-horizon mean-field games with unknown initial distributions and common noise, with favorable numerical results.
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