图强化学习算法及其在工业领域的应用研究综述
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北京化工大学

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TP391.4

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国家自然科学基金资助项目


A research review of graph reinforcement learning algorithms and their applications in the industrial field
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    摘要:

    强化学习在决策支持、组合优化及智能控制等领域的成功应用推动了其对复杂工业场景的探索,然而现有强化学习方法难以迁移到非欧几里得空间的图结构数据。图神经网络在学习图结构数据方面表现出了卓越的性能,为此,通过将图与强化学习结合将图结构数据引入强化学习任务中,丰富了强化学习的知识表征,为解决复杂工业过程问题提供了新范式。系统梳理了图强化学习算法在工业领域的研究进展,从算法架构层面归纳总结图强化学习算法并提炼出了三大主流范式,探讨了其在生产调度、工业知识图谱推理、工业互联网及电力系统领域的应用进展,并分析了当前该领域面临的挑战与未来的发展趋势。

    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.

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历史
  • 收稿日期:2024-12-15
  • 最后修改日期:2025-05-26
  • 录用日期:2025-04-21
  • 在线发布日期: 2025-06-03
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