Construction and Analysis of Decision Optimization Model based on Deep Reinforcement Learning for Collaborative School-Enterprise-Local Education Decision Making

Abstract

The school-enterprise model is a collaborative approach that links educational institutions, local education in nearby companies, and the community to provide students with strong practical learning opportunities. This paper's primary goal was to investigate the use of deep reinforcement learning (DRL) in the analysis and research of school-enterprise cooperation's present state as well as its mechanism. Distance-Driven Proximal Policy Optimization (DD-PPO) based school-enterprise collaboration model has been presented in an attempt to address the issue of the method through which schools and businesses collaborate. The study analyzed with the higher vocational colleges in Hangzhou City school-enterprise partnership method, the case creation and assessment were finished. Local education refers to educational institutions, such as higher vocational colleges that are situated within a specific area or city. It indicates that 55% of the businesses decided to "select fresh employees". The school-enterprise collaboration was judged as having a "collaboration effect is universal" by 50% of the schools. The school has developed its professional orientation in terms of cooperative content based on the demands of the business, which could offer ways for graduates to find jobs. This approach of "cooperation-employment" in management is very simplistic. It lacks certainty and support from national policy, as well as methodical planning. Because schools and businesses have varied ways of evaluating, it applies to departments, classes, instructors, and students. Because of this, it is hard to set up an adaptable and efficient system between businesses and schools. Additionally, it causes significant disparities in the practical operating skills of students in various courses and professions.