mlsanityData to 14 Oct 2026

Multi-Year Maintenance Planning for Large-Scale Infrastructure Systems: A Novel Network Deep Q-Learning Approach

arXiv 2507.18732 ↗First posted 24 Jul 2025math.OC cs.AI cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Infrastructure asset management is essential for sustaining the performance of public infrastructure such as road networks, bridges, and utility networks. Traditional maintenance and rehabilitation planning methods often face scalability and computational challenges, particularly for large-scale networks with thousands of assets under budget constraints. This paper presents a novel deep reinforcement learning (DRL) framework that optimizes asset management strategies for large infrastructure networks. By decomposing the network-level Markov Decision Process (MDP) into individual asset-level MDPs while using a unified neural network architecture, the proposed framework reduces computational complexity, improves learning efficiency, and enhances scalability. The framework directly incorporates annual budget constraints through a budget allocation mechanism, ensuring maintenance plans are both optimal and cost-effective. Through a case study on a large-scale pavement network of 68,800 segments, the proposed DRL framework demonstrates significant improvements over traditional methods like Progressive Linear Programming and genetic algorithms, both in efficiency and network performance. This advancement contributes to infrastructure asset management and the broader application of reinforcement learning in complex, large-scale environments.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 27 references that are in the map. First posted on arXiv 24 Jul 2025.

Builds on

8 of 27 references.

References · 8

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2017Value-Decomposition Networks For Cooperative Multi-Agent Learningcited by 503
  3. 2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 485
  4. 2019QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learningcited by 379
  5. 2020FACMAC: Factored Multi-Agent Centralised Policy Gradientscited by 178
  6. 2020Qatten: A General Framework for Cooperative Multiagent Reinforcement Learningcited by 81
  7. 2022Mean-Field Approximation of Cooperative Constrained Multi-Agent Reinforcement Learning (CMARL)cited by 5
  8. 2025CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for Sustainable Maritime Logisticscited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025Hierarchical Deep Reinforcement Learning Framework for Multi-Year Asset Management Under Budget Constraints
  2. 2022A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning
  3. 2024Maintenance Strategies for Sewer Pipes with Multi-State Degradation and Deep Reinforcement Learning
  4. 2024Maintenance Optimization for Asset Networks with Unknown Degradation Parameters ↗
  5. 2026Graph-Based Inference and Topology-Aware Multi-Agent Reinforcement Learning for Large-Scale Railway Network Management
  6. 2024InfraLib: Enabling Reinforcement Learning and Decision-Making for Large-Scale Infrastructure Management
  7. 2024Multi-agent deep reinforcement learning with centralized training and decentralized execution for transportation infrastructure management
  8. 2024Scalable Policies for the Dynamic Traveling Multi-Maintainer Problem with Alerts