InfraLib: Enabling Reinforcement Learning and Decision-Making for Large-Scale Infrastructure Management
This work addresses the problem of inefficient infrastructure management for policymakers and engineers by providing a tool for benchmarking decision-making approaches, though it is incremental as it builds on existing reinforcement learning methods.
The authors tackled the challenge of applying reinforcement learning to large-scale infrastructure management by developing InfraLib, an open-source framework that models infrastructure problems as sequential decision-making tasks, demonstrating its effectiveness through case studies on synthetic benchmarks and real-world road networks.
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.