用深度强化学习优化大规模基础设施维护计划，省时省力还省钱。
Multi-Year Maintenance Planning for Large-Scale Infrastructure Systems: A Novel Network Deep Q-Learning Approach
- 将整体网络拆成单个资产问题，用统一神经网络降低计算负担。
- 在68,800段路面数据上，效率和效果均优于传统算法。
- 适合需要长期预算约束下做大规模设施维护的工程管理者。
基础设施资产管理对保障道路网、桥梁和公用设施等公共设施性能至关重要。传统维护与修复规划方法在大规模网络（含数千个资产）且受年度预算限制时，常面临可扩展性和计算挑战。本文提出一种新型深度强化学习（DRL）框架，优化大规模基础设施网络的资产管理策略。通过将网络级马尔可夫决策过程（MDP）分解为个体资产级MDP，同时采用统一神经网络架构，该框架显著降低计算复杂度，提升学习效率并增强可扩展性。通过预算分配机制直接融入年预算约束，确保维护方案既最优又经济。在包含68,800个路段的大规模路面网络案例研究中，所提DRL框架在效率和网络性能方面均显著优于渐进线性规划与遗传算法。这一进展推动了基础设施资产管理的发展，并拓展了强化学习在复杂大型环境中的应用。
原文摘要 · Abstract (English)
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.
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