Intelligent System Application in Clinical Management of Medical Teaching Based on Deep Reinforcement Learning

When dealing with engineering projects, there are many kinds of planning schemes. There are many problems to be solved in the performance and economic conditions of these indicators and engineering contents we study. Recently, many researchers have made great achievements in optimizing various subject projects. One of its purposes is to optimize and improve intelligent multi-objectives to make it more effective. Therefore, it is of great significance to develop intelligent multi-objective projects in academia and more engineering fields. The purpose of this study is to make efforts to strengthen the learning and construction of rapidly developing multi-objective optimization programs, and closely link these programs with neural network, fuzzy technology, interactive technology, and give a lot of examples of multi-objective optimization improvement program methods, which can eliminate the uncertainty in the multi-objective project plan, The purpose is to solve some difficult problems of multi-objective optimization and give the best suggestions. In this study, the strengthening of learning construction is combined with the clinical experiment, and most of this teaching method is put in the clinical management experiment of medical teaching. At the end of this study, we compared the performance of deep reinforcement learning with its dual structure version and interleaved structure version in different medical teaching clinical management environments and make a detailed analysis. Deep reinforcement learning provides a new idea for the improvement of intelligent multi-objective optimization and clinical management of medical teaching.