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This paper studies two fundamental problems in regularized Graphon Mean-Field Games (GMFGs).
Approximately solving mean field games via entropy-regularized deep reinforcement learning
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Scaling up mean field games with online mirror descent
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Q-learning in regularized mean-field games
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Stochastic graphon games: I. the static case
R. Carmona, D. B. Cooney, C. V. Graves, and M. Lauriere · 2022
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A review of off-policy evaluation in reinforcement learning
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Policy mirror ascent for efficient and independent learning in mean field games
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Oracle-free reinforcement learning in mean-field games along a single sample path
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