A differentiable neural framework for learning state- and time-dependent parameters of finite-state mean field games from population trajectories via implicit differentiation.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Proves existence, uniqueness under convexity, fictitious-play approximation, and vanishing-limit convergence for entropy-regularized equilibria in rank-based mean-field optimal-switching games.
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Neural Parameter Calibration for Finite-State Mean Field Games
A differentiable neural framework for learning state- and time-dependent parameters of finite-state mean field games from population trajectories via implicit differentiation.
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Mean Field Competition of Optimal Switching: The Vanishing Entropy Regularization Approach
Proves existence, uniqueness under convexity, fictitious-play approximation, and vanishing-limit convergence for entropy-regularized equilibria in rank-based mean-field optimal-switching games.