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Mean-field games have been used as a theoretical tool to obtain an approximate Nash equilibrium for symmetric and anonymous $N$-player games.
Analysis of temporal-diffference learning with function approximation
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Markov game approach for multi-agent competitive bidding strategies in electricity market
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Markov chains and mixing times , volume 107
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Learning deep mean field games for modeling large population behavior
Yang, J., Ye, X., Trivedi, R., Xu, H., and Zha, H · 2018
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Model-free mean-field reinforcement learning: mean-field mdp and mean-field q-learning
Carmona, R., Laurière, M., and Tan, Z · 2019
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Actor-critic provably finds nash equilibria of linear-quadratic mean-field games
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A theory of regularized markov decision processes
Geist, M., Scherrer, B., and Pietquin, O · 2019
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Reinforcement learning in stationary mean-field games
Subramanian, J. and Mahajan, A · 2019
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Learning while playing in mean-field games: Convergence and optimality
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Q-learning in regularized mean-field games
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Simple and optimal methods for stochastic variational inequalities, ii: Markovian noise and policy evaluation in reinforcement learning
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Mean-field multi-agent reinforcement learning: A decentralized network approach
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Independent learning in stochastic games
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From poincaré recurrence to convergence in imperfect information games: Finding equilibrium via regularization
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