## 1 Introduction  
In recent years, massive open online courses such as MOOCs, Khan Academy, MuKe, and other online learning systems have been designed and developed for educational purposes. One of the fundamental techniques in the design of individual learning paths is to arrange sequence of learning contents for improving a student's knowledge or skill in particular subjects or degree courses. To reasonably arrange a personal learning sequence of learning contents, one's implicit learning behavior patterns from the student's online learning behavior data need to be taken into account. Compared to the prevalence of a personal learning path in online systems, similar recommendation methods in traditional teaching and learning are still very limited in cloud computing services. In traditional classroom teaching, teachers often arrange learning contents from experience, as the learning details of individual students are unknown and beyond their control. Of course, a teacher may receive miscellaneous messages including questions and answers, in-class assessments, and students' facial expressions, gestures, and body movements, that enable them to evaluate the students' learning performance. However, such information is often sketchy, and cannot cover every student or track everyone's learning details. This makes teachers often unable to design a teaching path with a fine granularity. With the development of e-learning systems and teaching-aided systems, the learning behavior data of students can be observed and investigated. For example, clickers and mobile devices support in-class [1] question-answer interactions about some specific learning contents. Camera and body sensors [2] are utilized to capture learning facial expressions and body language to evaluate the teaching effects, respectively. After class,electronic exams and exercises provide delayed but quantitative learning responses for students. Therefore, we can build learning models of students based on this information, and explore related teaching or learning recommendation methods for better performance, including via online systems. The main feature of traditional teaching is its one-to-many teaching structure between teacher and students, which means that the teaching path, e.g., a sequence of learning contents to ensure the maximum benefit for a given group of students, is completely different from the personal learning path in online learning systems. Thus, studies seeking to optimize personal learning paths cannot provide an effective solution for teachers. Some researchers have proposed a group learning path or teaching path based on the student's personal information [3,4], by combining the strengths or similarities of each student to achieve better results in group tasks. Generally, there is no target-oriented teaching planning scheme which helps teachers arrange the learning contents of a course (knowledge points) to achieve predefined targets in practical classroom teaching. This paper uses a DRL method to perform target-oriented teaching path planning for teachers. First, our scheme is intended for traditional classroom teaching, e.g., for the maximum benefits of the entire class. Second, our scheme is target-oriented, which means some quantitative targets, such as teaching time, passing rate, average score, and so on, are invoked as optimization goals. Third, the relevance of various knowledge points is considered in the optimization. The learning of one knowledge point is affected by that of its anterior knowledge points. Finally, our method is independent of student models, in which the DRL algorithm directly generates planning based on feedback from students--not student models. We conducted experiments with a variety of commonly used student models as virtual students, and the results compared with the baseline methods prove that our method is applicable, efficient, and versatile.