基于公用事业的基础设施维护优化深度多目标增强学习
Jesse van Remmerden1, Maurice Kenter2, Diederik M Roijers2,3
1Information Systems IE&IS, Eindhoven University of Technology, De Zaale, 5600 MB Eindhoven, The Netherlands.
Neural computing & applications
|October 13, 2025
概括
本研究介绍了基础设施维护的多目标深度集中式多代理主体关键 (MO-DCMAC). MO-DCMAC优化了多个目标的政策,在成本和安全评估方面表现优于传统方法.
科学领域:
- 人工智能的人工智能
- 土木工程 土木工程是指土木工程.
- 运营研究 运营研究
背景情况:
- 基础设施维护传统上使用单一目标的强化学习 (RL),通常将成本和安全等多个目标结合到一个奖励中.
- 这种奖励塑造可以过度简化资产管理的复杂决策过程.
研究的目的:
- 引入多目标深度集中式多代理主体-关键 (MO-DCMAC) 以直接多目标优化基础设施维护.
- 即使使用非线性实用函数,也可以实现优化,改善了传统的RL限制.
主要方法:
- 开发了MO-DCMAC,一种新的多目标强化学习方法.
- 使用值和故障模式,效应和关键性分析 (FMECA) 实用函数评估了MO-DCMAC.
- 在各种维护环境中进行了测试,包括阿姆斯特丹历史悠久的码头墙壁,与基于规则的政策进行比较.
主要成果:
- MO-DCMAC有效地优化维护策略,同时实现多个目标.
- 与现有的基于规则的启发式策略相比,在各种场景中表现出卓越的性能.
- 验证了该方法在不同实用功能和复杂环境中的有效性.
结论:
- 在基础设施维护优化方面,MO-DCMAC比单一目标RL提供了显著的进步.
- 该方法为平衡成本和安全等竞争目标提供了更强大和更有效的方法.
- 这项研究为更复杂,更有效的资产管理策略铺平了道路.
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