We present a simulation-based approach for solution of mean field games (MFGs), using the framework of empirical game-theoretical analysis (EGTA). Our method employs a version of the double oracle, iteratively adding strategies based on best response to the equilibrium of the empirical MFG among strategies considered so far. The empirical game equilibrium is computed with a query-based method, rather than maintaining an explicit payoff matrix as in typical EGTA methods. We show that NE exist in the empirical MFG and study the convergence of iterative EGTA to NE of the full MFG. We test the performance of iterative EGTA in various games and show that it outperforms in terms of iterations of strategy introduction. Finally, we discuss the limitations of applying iterative EGTA to MFGs as well as potential future research directions.

翻译：我们利用实证游戏理论分析框架(EGTA)提出一种模拟方法来解决中值野外游戏(MFGs),我们的方法采用一种双极手法,根据迄今所考虑的战略对实证外地游戏(MFGs)的平衡作出最佳反应,反复增加战略;实证游戏平衡是用查询法计算出来的,而不是像欧洲GTA的典型方法那样,保持一个明确的回报矩阵;我们表明,在实证MFG中存在NE,并研究整个MFG的迭代 EGTA与NE的融合。我们测试了迭代欧洲GTA在各种游戏中的性能,并表明它在战略导言的迭代性方面表现优于它。最后,我们讨论了对MFGs应用反复的 EGTA的局限性,以及潜在的未来研究方向。