Quantitative evaluation of machine learning capability based on a differential game problem

With the development of artificial intelligence, various machine learning algorithms have emerged, but in the past, it was very inefficient to use machine learning methods to solve problems such as optimal control and problems of game theory, so a method that can quantitatively evaluate the machine learning capability is proposed, which can significantly improve the efficiency of existing algorithms. In this paper, a differential game problem based on the sheep-dog two-dimensional motion model is established and five related questions are answered according to the constraints in the problem. Meanwhile, the algorithm is analyzed quantitatively and the corresponding results are obtained. The paper also verifies that several models, equations of motion and machine learning algorithms established in this paper are reliable through sensitivity analysis.