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Mean field type games (MFTGs) describe Nash equilibria between large coalitions: each coalition consists of a continuum of cooperative agents who maximize the average reward of their coalition while interacting non-cooperatively with a finite number of other coalitions.
Non-cooperative games
John Nash · 1951
Earlier work this paper cites.
Equilibrium in a stochastic n n -person game
Arlington M Fink · 1964
Earlier work this paper cites.
Game theory
Drew Fudenberg and Jean Tirole · 1991
Earlier work this paper cites.
Nash Q-learning for general-sum stochastic games
Junling Hu and Michael P Wellman · 2003
Earlier work this paper cites.
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Minyi Huang, Roland P. Malhamé, and Peter E. Caines · 2006
Earlier work this paper cites.
Mean field games
Jean-Michel Lasry and Pierre-Louis Lions · 2007
Earlier work this paper cites.
The complexity of computing a Nash equilibrium
Constantinos Daskalakis, Paul W Goldberg, and Christos H Papadimitriou · 2009
Earlier work this paper cites.
Mean field games and mean field type control theory , volume 101
Alain Bensoussan, Jens Frehse, and Phillip Yam · 2013
Earlier work this paper cites.
Mean field games models—a brief survey
Diogo A Gomes and João Saúde · 2014
Earlier work this paper cites.
Fictitious self-play in extensive-form games
Johannes Heinrich, Marc Lanctot, and David Silver · 2015
Earlier work this paper cites.
Risk-sensitive mean-field-type games with Lp-norm drifts
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Earlier work this paper cites.
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Earlier work this paper cites.
Mean-field-game model for botnet defense in cyber-security
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Earlier work this paper cites.
Continuous control with deep reinforcement learning
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Earlier work this paper cites.
Mastering the game of Go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Earlier work this paper cites.
Mean-field-type games
Hamidou Tembine · 2017
Earlier work this paper cites.
Mean field control and mean field game models with several populations
Alain Bensoussan, Tao Huang, and Mathieu Laurière · 2018
Earlier work this paper cites.
Probabilistic Theory of Mean Field Games with Applications I-II
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