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Published on: February 6, 2020
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A maintenance planning framework using online and offline deep reinforcement learning
Zaharah A Bukhsh1, Hajo Molegraaf2, Nils Jansen3
1Eindhoven University of Technology, Eindhoven, The Netherlands.
Neural Computing & Applications
|June 30, 2025
Summary
This study introduces a deep reinforcement learning (DRL) approach for optimal water pipe rehabilitation planning. DRL policies significantly reduce costs and failures compared to traditional methods, with offline learning showing further improvements.
Area of Science:
- Asset Management
- Artificial Intelligence
- Civil Engineering
Background:
- Cost-effective asset management is crucial across industries.
- Deteriorating water pipes pose significant infrastructure challenges.
- Optimizing rehabilitation strategies is essential for extending asset lifespan and reducing failures.
Purpose of the Study:
- To develop a deep reinforcement learning (DRL) solution for optimal water pipe rehabilitation.
- To compare online and offline DRL approaches for rehabilitation planning.
- To evaluate DRL policy performance against traditional methods.
Main Methods:
- Implemented online DRL with an agent interacting in a simulated pipe environment.
- Utilized deep Q-learning (DQN) for policy optimization in the online setting.
- Employed conservative Q-learning with static data (DQN replay) for offline DRL.
Main Results:
- DRL-based policies outperformed standard preventive, corrective, and greedy planning.
- Offline DRL, using fixed replay data, demonstrated enhanced performance.
- Water pipe deterioration data proved valuable for offline policy learning.
Conclusions:
- DRL offers a powerful tool for optimizing water pipe rehabilitation strategies.
- Offline DRL presents a promising approach, leveraging existing data for improved policy learning.
- Further fine-tuning with simulation can enhance offline DRL policies.
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