A differentiable neural framework for learning state- and time-dependent parameters of finite-state mean field games from population trajectories via implicit differentiation.
Applications of Mean Field Games in Financial Engineering and Economic Theory.arXiv preprint, 2020
2 Pith papers cite this work. Polarity classification is still indexing.
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Reformulates potential mean field games on finite graphs as a finite-dimensional initial-value optimization problem with ODE dynamics constraints, solved via neural networks.
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Discrete Mean Field Games on Finite Graphs as Initial Value Optimization
Reformulates potential mean field games on finite graphs as a finite-dimensional initial-value optimization problem with ODE dynamics constraints, solved via neural networks.