ad auction problem
Reviews: Learning Mean-Field Games
This paper considers learning in mean-field games (MFG). MFGs take the limit of an infinite number of agents, which are considered indistinguishable. Based on a motivating example consisting of a repeated Ad auction problem, the authors introduce a "general" mean-field game (GMFG), a model-free version of the standard MFG. The authors revisit standard Q-Learning and a soft version of it, and provide convergence and complexity results of such an algorithm. These methods are compared numerically on the auction problem together with a recently proposed approach and show better performance.