A Deep Reinforcement Learning-based Algorithm for Balanced Allocation of Teaching Resources in International Economics
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| Title: | A Deep Reinforcement Learning-based Algorithm for Balanced Allocation of Teaching Resources in International Economics |
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| Authors: | Wenxi Xu |
| Source: | WSEAS TRANSACTIONS ON COMPUTER RESEARCH. 13:357-365 |
| Publisher Information: | World Scientific and Engineering Academy and Society (WSEAS), 2025. |
| Publication Year: | 2025 |
| Description: | The rapid development of online education technology and the increasing demand for international education require a more flexible and intelligent allocation of teaching resources to adapt to the constantly changing teaching environment and student needs. Therefore, a balanced allocation algorithm of international economics teaching resources based on deep and strong learning is proposed. That is, the joint allocation scheme of international economics teaching resources is designed by using deep reinforcement learning, and the balanced allocation algorithm of international economics teaching resources is generated, thus realizing the balanced allocation of teaching resources. The experimental results show that the RF service rate of the designed in-depth reinforcement learning balanced allocation algorithm for international economics teaching resources is low, which proves that the designed balanced allocation algorithm for teaching resources has good performance and reliability, the research results of this paper not only provide new ideas and methods for the allocation of teaching resources in international economics but also provide valuable experience and inspiration for the allocation of teaching resources in other fields, which will help promote the optimization and efficient utilization of educational resources and promote the sustainable development of the education industry. |
| Document Type: | Article |
| Language: | English |
| ISSN: | 2415-1521 1991-8755 |
| DOI: | 10.37394/232018.2025.13.33 |
| Rights: | URL: https://wseas.com/journals/cr/2025/a665118-313.pdf |
| Accession Number: | edsair.doi...........8ac91769a47061ce2bc72425c0504e48 |
| Database: | OpenAIRE |
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