国防科技大学学报2025，Vol.47Issue(4)：76-90,15.DOI:10.11887/j.issn.1001-2486.24120028
图强化学习算法及其在工业领域的应用研究综述
Research review of graph reinforcement learning algorithms and their applications in the industrial field
摘要
Abstract
Successful application of reinforcement learning in decision support,combinatorial optimization,and intelligent control has driven its exploration in complex industrial scenarios.However,existing reinforcement learning methods face challenges in adapting to graph-structured data in non-Euclidean spaces.Graph neural networks have demonstrated exceptional performance in learning graph-structured data.By integrating graphs with reinforcement learning,graph-structured data was introduced into reinforcement learning tasks,enriching knowledge representation in reinforcement learning and offering a novel paradigm for addressing complex industrial process problems.The research progress of graph reinforcement learning algorithms in industrial domains was systematically reviewed,summarized graph reinforcement learning algorithms from the perspective of algorithm architecture and extracted three mainstream paradigms,explored their applications in production scheduling,industrial knowledge graph reasoning,industrial internet,power system and other fields,and analyzed current challenges alongside future development trends in this field.关键词
强化学习/图神经网络/图强化学习/图结构数据Key words
reinforcement learning/graph neural network/graph reinforcement learning/graph structured data分类
信息技术与安全科学引用本文复制引用
李大字,刘子博,包琰洋,董才波,徐昕..图强化学习算法及其在工业领域的应用研究综述[J].国防科技大学学报,2025,47(4):76-90,15.基金项目
国家自然科学基金资助项目(62273026) （62273026）
