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Mean Field Games (MFGs) have been introduced to efficiently approximate games with very large populations of strategic agents.
Iterative solution of games by fictitious play
George W Brown · 1951
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The complexity of computing a Nash equilibrium
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Large population stochastic dynamic games: closed-loop McKean-Vlasov systems and the Nash certainty equivalence principle
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General duality between optimal control and estimation
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Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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A fully discrete semi-Lagrangian scheme for a first order mean field game problem
Elisabetta Carlini and Francisco J. Silva · 2014
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René Carmona, Jean-Pierre Fouque, and Li-Hsien Sun · 2015
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Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Deep reinforcement learning from self-play in imperfect-information games
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Mastering the game of Go with deep neural networks and tree search
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Model-free reinforcement learning for non-stationary mean field games
Rajesh K Mishra, Deepanshu Vasal, and Sriram Vishwanath · 2020
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Fictitious play for mean field games: Continuous time analysis and applications
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A policy iteration method for mean field games
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Approximately solving mean field games via entropy-regularized deep reinforcement learning
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