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Mean-field games (MFG) have become significant tools for solving large-scale multi-agent reinforcement learning problems under symmetry.
Approximately solving mean field games via entropy-regularized deep reinforcement learning
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Markov game approach for multi-agent competitive bidding strategies in electricity market
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Learning while playing in mean-field games: Convergence and optimality
Q. Xie, Z. Yang, Z. Wang, and A. Minca · 2021
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Q-learning in regularized mean-field games
B. Anahtarci, C. D. Kariksiz, and N. Saldi · 2022
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A general framework for learning mean-field games
X. Guo, A. Hu, R. Xu, and J. Zhang · 2022
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Simple and optimal methods for stochastic variational inequalities, ii: Markovian noise and policy evaluation in reinforcement learning
G. Kotsalis, G. Lan, and T. Li · 2022
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Scalable deep reinforcement learning algorithms for mean field games
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Probabilistic theory of mean field games with applications I-II
R. Carmona, F. Delarue, et al · 2018
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Markov–nash equilibria in mean-field games with discounted cost
N. Saldi, T. Basar, and M. Raginsky · 2018
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Graphon mean field games and the gmfg equations: ε \varepsilon -nash equilibria
P. E. Caines and M. Huang · 2019
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Learning mean-field games
X. Guo, A. Hu, R. Xu, and J. Zhang · 2019
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F. Parise and A. Ozdaglar · 2019
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The starcraft multi-agent challenge
M. Samvelyan, T. Rashid, C. Schroeder de Witt, G. Farquhar, N. Nardelli, T. G. J. Rudner, C.-M. Hung, P. H. S. Torr, J. Foerster, and S. Whiteson · 2019
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Provably efficient exploration in policy optimization
Q. Cai, Z. Yang, C. Jin, and Z. Wang · 2020
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M. Laurière, S. Perrin, S. Girgin, P. Muller, A. Jain, T. Cabannes, G. Piliouras, J. P’erolat, R. Elie, O. Pietquin, and M. Geist · 2022
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A mean-field game approach to cloud resource management with function approximation
W. Mao, H. Qiu, C. Wang, H. Franke, Z. Kalbarczyk, R. Iyer, and T. Basar · 2022
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Scaling mean field games by online mirror descent
J. Pérolat, S. Perrin, R. Elie, M. Laurière, G. Piliouras, M. Geist, K. Tuyls, and O. Pietquin · 2022
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Generalization in mean field games by learning master policies
S. Perrin, M. Laurière, J. Pérolat, R. Élie, M. Geist, and O. Pietquin · 2022
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A multi-agent deep reinforcement learning framework for algorithmic trading in financial markets
A. Shavandi and M. Khedmati · 2022
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The complexity of markov equilibrium in stochastic games
C. Daskalakis, N. Golowich, and K. Zhang · 2023
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G. Dayanikli and M. Lauriere · 2023
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J. Huang, B. Yardim, and N. He · 2023
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Policy mirror descent for reinforcement learning: Linear convergence, new sampling complexity, and generalized problem classes
G. Lan · 2023
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Oracle-free reinforcement learning in mean-field games along a single sample path
M. A. U. Zaman, A. Koppel, S. Bhatt, and T. Basar · 2023
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Learning regularized monotone graphon mean-field games
F. Zhang, V. Y. Tan, Z. Wang, and Z. Yang · 2023
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Model-based rl for mean-field games is not statistically harder than single-agent rl, 2024
J. Huang, N. He, and A. Krause · 2024
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Learning in mean field games: A survey, 2024
M. Laurière, S. Perrin, J. Pérolat, S. Girgin, P. Muller, R. Élie, M. Geist, and O. Pietquin · 2024
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When is mean-field reinforcement learning tractable and relevant?, 2024
B. Yardim, A. Goldman, and N. He · 2024
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