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Learning Mean-Field Games through Mean-Field Actor-Critic Flow

arXiv — cs.LG•Monday, October 27, 2025 at 4:00:00 AM
PositiveArtificial Intelligence
The introduction of the Mean-Field Actor-Critic (MFAC) flow marks a significant advancement in the study of mean-field games, blending reinforcement learning with optimal transport techniques. This innovative framework allows for continuous-time learning dynamics, enhancing how control and value functions evolve through gradient-based updates. This development is crucial as it opens new avenues for solving complex game-theoretic problems, potentially impacting various fields such as economics and artificial intelligence.
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