A differentiable neural framework for learning state- and time-dependent parameters of finite-state mean field games from population trajectories via implicit differentiation.
Applied Mathematics & Optimization74(3), 669–692 (2016)
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A monograph develops the probabilistic and control-theoretic framework connecting multi-agent reinforcement learning to mean field control, including analyses of Q-learning, policy gradients, and numerical methods for linear-quadratic and general models.
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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 Reinforcement Learning
A monograph develops the probabilistic and control-theoretic framework connecting multi-agent reinforcement learning to mean field control, including analyses of Q-learning, policy gradients, and numerical methods for linear-quadratic and general models.