InfraLib: Enabling Reinforcement Learning and Decision-Making for Large-Scale Infrastructure Management

TL;DR

InfraLib reframes large-scale infrastructure management as a hierarchy of POMDPs with a shared budget, enabling uncertainty-aware planning over a finite horizon $H$. It provides an open-source, modular Python toolkit with Weibull deterioration, nonlinear repair costs, and cyclic budget models, plus Gymnasium-style RL environments and a human-in-the-loop interface for data collection and policy evaluation. The framework is demonstrated on scalable benchmarks (e.g., LargeSys-100K) and real-world-inspired scenarios (Manhattan road network), showing promising gains for RL and related data-driven methods while supporting expert input and comparability against baselines. Overall, InfraLib offers a practical path to integrating learning-based decision-making into asset management workflows, with extensible components to model diverse infrastructure settings.

Abstract

Efficient management of infrastructure systems is crucial for economic stability, sustainability, and public safety. However, infrastructure sustainment is challenging due to the vast scale of systems, stochastic deterioration of components, partial observability, and resource constraints. Decision-making strategies that rely solely on human judgment often result in suboptimal decisions over large scales and long horizons. While data-driven approaches like reinforcement learning offer promising solutions, their application has been limited by the lack of suitable simulation environments. We present InfraLib, an open-source modular and extensible framework that enables modeling and analyzing infrastructure management problems with resource constraints as sequential decision-making problems. The framework implements hierarchical, stochastic deterioration models, supports realistic partial observability, and handles practical constraints including cyclical budgets and component unavailability. InfraLib provides standardized environments for benchmarking decision-making approaches, along with tools for expert data collection and policy evaluation. Through case studies on both synthetic benchmarks and real-world road networks, we demonstrate InfraLib's ability to model diverse infrastructure management scenarios while maintaining computational efficiency at scale.

Figures (5)

• Figure 1: Component condition index over time from InfraLib simulations illustrating (a) intermittent unavailability periods (red regions) and (b) catastrophic failure (at time step 45).
• Figure 2: Snapshot from InfraLib's analysis and data collection human interface.
• Figure 3: Average reward values during training for the three evaluated RL algorithms.
• Figure 4: A sample rollout of the DQN policy on the environment with five components.
• Figure 5: Simulated condition indices of Manhattan's road network using InfraLib. The visualization shows network conditions at simulation steps 0, 25, 50, and 75, comparing scenarios without maintenance (top) and with maintenance under a fixed cyclic budget allocation (bottom).