蒙特卡洛算法也常用于机器学习，特别是强化学习的算法中。一般情况下，针对得到的样本数据集建立相对模糊的模型，通过蒙特卡洛方法对于模型中的参数进行选取，使之于原始数据的残差尽可能的小。从而达到建立模型拟合样本的目的。(Monte Carlo algorithm is also commonly used in machine learning, especially in reinforcement learning algorithm. In general, a relatively fuzzy model is established for the obtained sample data set, and the parameters in the model are selected by Monte Carlo method so that the residual error of the original data is as small as possible. Thus, the purpose of establishing the model fitting sample is achieved.)