使用在线和离线深度强化学习的维护规划框架
Zaharah A Bukhsh1, Hajo Molegraaf2, Nils Jansen3
1Eindhoven University of Technology, Eindhoven, The Netherlands.
Neural computing & applications
|June 30, 2025
概括
本研究引入了深度强化学习 (DRL) 方法,用于最佳的水管改造规划. 与传统方法相比,DLR政策显著降低了成本和失败,线下学习显示了进一步的改进.
科学领域:
- 资产管理资产管理.
- 人工智能的人工智能
- 土木工程 土木工程是指土木工程.
背景情况:
- 在各个行业中,具有成本效益的资产管理至关重要.
- 恶化的水管道给基础设施带来了重大挑战.
- 优化康复策略对于延长资产寿命和减少故障至关重要.
研究的目的:
- 开发一个深度强化学习 (DRL) 解决方案,以实现最佳的水管改造.
- 为了比较在线和离线的DRL方法用于康复规划.
- 与传统方法相比,评估DRL政策的表现.
主要方法:
- 实现在线DRL与在模拟管道环境中相互作用的代理.
- 利用深度Q学习 (DQN) 在在线环境中优化政策.
- 使用保守的Q学习与静态数据 (DQN重播) 离线DRL.
主要成果:
- 基于DRL的政策表现优于标准的预防,纠正和贪规划.
- 离线DRL,使用固定重播数据,证明了增强的性能.
- 水管恶化数据对于离线政策学习非常有价值.
结论:
- DRL为优化水管改造策略提供了一个强大的工具.
- 离线DRL提出了一个有前途的方法,利用现有数据来改善政策学习.
- 通过模拟进行进一步的微调,可以增强线下DRL策略.
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