Ruimeng Hu | Department of Statistics, Columbia University
Deep fictitious play for stochastic differential games
Differential games, as an offspring of game theory and optimal control, provide the modeling and analysis of conflict in the context of a dynamic system. Computing Nash equilibria is one of the core objectives in differential games, with a major bottleneck coming from the notorious intractability of N-player games. This leads to the difficulty of the curse of dimensionality, which will be overcome by the presented theory and algorithms of deep fictitious play using machine learning tools.