We introduce two algorithms based on a policy iteration method to numerically solve time-dependent Mean Field Game systems of partial differential equations with non-separable Hamiltonians. We prove the convergence of such algorithms in sufficiently small time intervals with Banach fixed point method. Moreover, we prove that the convergence rates are linear. We illustrate our theoretical results by numerical examples, and we discuss the performance of the proposed algorithms.

翻译：我们引入了两种基于政策迭代法的算法,以数字方式解决与不可分离的汉密尔顿人部分差分方程的取决于时间的中值场游戏系统。我们证明这种算法在足够短的时间内与巴纳赫固定点法相融合。此外,我们证明趋同率是线性的。我们用数字例子来说明我们的理论结果,我们讨论提议的算法的性能。