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Non-cooperative and cooperative games with a very large number of players have many applications but remain generally intractable when the number of players increases.
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Synthesis of Cucker-Smale type flocking via mean field stochastic control theory: Nash equilibria
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Achdou, Y., Camilli, F., and Capuzzo-Dolcetta, I. (2012) · 2012
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Explicit solutions of some linear-quadratic mean field games
Bardi, M. (2012) · 2012
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Electrical vehicles in the smart grid: A mean field game analysis
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Bensoussan, A., Frehse, J., Yam, P., et al. (2013) · 2013
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Mean field games with nonlinear mobilities in pedestrian dynamics
Burger, M., Francesco, M., Markowich, P., and Wolfram, M.-T. (2013) · 2013
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Mean field forward-backward stochastic differential equations
Carmona, R. and Delarue, F. (2013) · 2013
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The derivation of ergodic mean field game equations for several populations of players
Feleqi, E. (2013) · 2013
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Mean field analysis of multi-armed bandit games
Gummadi, R., Johari, R., Schmit, S., and Yu, J. Y. (2013) · 2013
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Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013) · 2013
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ϵ \textstyle\epsilon -Nash mean field game theory for nonlinear stochastic dynamical systems with major and minor agents
Nourian, M. and Caines, P. E. (2013) · 2013
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Opinion dynamics and stubbornness through mean-field games
Stella, L., Bagagiolo, F., Bauso, D., and Como, G. (2013) · 2013
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Learning in mean-field games
Yin, H., Mehta, P. G., Meyn, S. P., and Shanbhag, U. V. (2013) · 2013
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PDE models in macroeconomics
Achdou, Y., Buera, F., Lasry, J.-M., Lions, P.-L., and Moll, B. (2014) · 2014
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Mean-field games and dynamic demand management in power grids
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Linear-quadratic N-person and mean-field games with ergodic cost
Bardi, M. and Priuli, F. S. (2014) · 2014
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A fully discrete semi-Lagrangian scheme for a first order mean field game problem
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Mean field equilibria of dynamic auctions with learning
Iyer, K., Johari, R., and Sundararajan, M. (2014) · 2014
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Markov decision processes: discrete stochastic dynamic programming
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Augmented Lagrangian methods for transport optimization, mean field games and degenerate elliptic equations
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A semi-Lagrangian scheme for a degenerate second order mean field game system
Carlini, E. and Silva, F. J. (2015) · 2015
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Multi-population mean field games systems with Neumann boundary conditions
Cirant, M. (2015) · 2015
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Energy-efficient resource management in ultra dense small cell networks: A mean-field approach
Samarakoon, S., Bennis, M., Saad, W., Debbah, M., and Latva-Aho, M. (2015) · 2015
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A long-term mathematical model for mining industries
Achdou, Y., Giraud, P.-N., Lasry, J.-M., and Lions, P.-L. (2016) · 2016
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Achdou, Y. and Laurière, M. (2016) · 2016
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Bensoussan, A., Chau, M., and Yam, S. (2016) · 2016
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A segregation problem in multi-population mean field games
Cardaliaguet, P., Porretta, A., and Tonon, D. (2016) · 2016
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Carmona, R., Delarue, F., and Lacker, D. (2016) · 2016
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Finite state mean field games with major and minor players
Carmona, R. and Wang, P. (2016) · 2016
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Carmona, R. A. and Zhu, X. (2016) · 2016
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Extended deterministic mean-field games
Gomes, D. A. and Voskanyan, V. K. (2016) · 2016
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Goodfellow, I., Bengio, Y., and Courville, A. (2016) · 2016
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Mean-field games for distributed caching in ultra-dense small cell networks
Hamidouche, K., Saad, W., Debbah, M., and Poor, H. V. (2016) · 2016
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Mean-field-game model for botnet defense in cyber-security
Kolokoltsov, V. N. and Bensoussan, A. (2016) · 2016
Bayesian multi-type mean field multi-agent imitation learning
Yang, F., Vereshchaka, A., Chen, C., and Dong, W. (2020) · 2020
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Anahtarci, B., Kariksiz, C. D., and Saldi, N. (2021) · 2021
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Mean-field-type Games for Engineers
Barreiro-Gomez, J. and Tembine, H. (2021) · 2021
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Cacace, Simone, Camilli, Fabio, and Goffi, Alessandro (2021) · 2021
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Graphon mean field games and their equations
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Mean field game based control of dispersed energy storage devices with constrained inputs
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Quantitative propagation of chaos for mean field Markov decision process with common noise
Motte, M. and Pham, H. (2023) · 2023
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Graphon games: A statistical framework for network games and interventions
Parise, F. and Ozdaglar, A. (2023) · 2023
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Efficient model-based multi-agent mean-field reinforcement learning
Pásztor, B., Krause, A., and Bogunovic, I. (2023) · 2023
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Partially observable mean field multi-agent reinforcement learning based on graph attention network for uav swarms
Yang, M., Liu, G., Zhou, Z., and Wang, J. (2023) · 2023
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Maximum causal entropy inverse reinforcement learning for mean-field games
Anahtarci, B., Kariksiz, C. D., and Saldi, N. (2024) · 2024
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Mean field games master equations: from discrete to continuous state space
Bertucci, C. and Cecchin, A. (2024) · 2024
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Correlated equilibria for mean field games with progressive strategies
Bonesini, O., Campi, L., and Fischer, M. (2024) · 2024
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Connecting gans, mean-field games, and optimal transport
Cao, H., Guo, X., and Laurière, M. (2024) · 2024
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Exploration noise for learning linear-quadratic mean field games
Delarue, F. and Vasileiadis, A. (2024) · 2024
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Mean-field multiagent reinforcement learning: A decentralized network approach
Gu, H., Guo, X., Wei, X., and Xu, R. (2024) · 2024
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MF-OMO: An optimization formulation of mean-field games
Guo, X., Hu, A., and Zhang, J. (2024) · 2024
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On imitation in mean-field games
Ramponi, G., Kolev, P., Pietquin, O., He, N., Laurière, M., and Geist, M. (2024) · 2024
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Learning optimal policies in potential mean field games: Smoothed policy iteration algorithms
Tang, Q. and Song, J. (2024) · 2024
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Independent RL for cooperative-competitive agents: A mean-field perspective
uz Zaman, M. A., Koppel, A., Laurière, M., and Basar, T. (2024) · 2024
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Population-aware Online Mirror Descent for Mean-Field Games by Deep Reinforcement Learning
Wu, Z., Laurière, M., Chua, S. J. C., Geist, M., Pietquin, O., and Mehta, A. (2024) · 2024
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Learning regularized monotone graphon mean-field games
Zhang, F., Tan, V., Wang, Z., and Yang, Z. (2024) · 2024
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Socio-economic applications of finite state mean field games
Gomes, D., Velho, R. M., and Wolfram, M.-T. (2014a) · 2028
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When is mean-field reinforcement learning tractable and relevant?
Yardim, B., Goldman, A., and He, N. (2024) · 2046
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