WUIRL: Reconstructing Wildfire Structure Ignition in the Wildland–Urban Interface with Reinforcement Learning

Wildfires are increasingly burning through the wildland–urban interface (WUI), destroying homes and other structures where development meets wildland vegetation. Modeling which buildings are most at risk remains difficult because the physical mechanisms governing fire spread and ignition in the WUI are incompletely understood, data on WUI and wildland fuel are coarse, and historical fire progression is too sparsely observed to learn directly. Supervised methods currently map building, vegetation, and weather data to structure outcomes. However, these static inputs cannot capture the mechanisms by which fire reaches a structure over time, which is necessary to predict ignition and determine an appropriate method to mitigate risk. Capturing the ignition process requires temporal ignition data, which is rarely recorded, leaving the process that determines survival and guides mitigation largely undocumented. Therefore, we introduce Wildland–Urban Interface Reinforcement Learning (WUIRL), a framework that reconstructs how fire reaches and ignites buildings from limited observations by pairing proximal policy optimization (PPO) with a physical ignition model. The learned policy selects pathways through which burning vegetation, neighboring structures, and external sources expose buildings to heat, and the physical model determines how each building responds. WUIRL thus links structural vulnerability and evolving exposure, reconstructing progression from recorded damage and coarse timing without complete exposure histories. We apply WUIRL to the 2025 Eaton fire, recovering 78.9% of buildings labeled as ignited from damage records while achieving the highest balanced accuracy among the evaluated policies. The reconstruction is most sensitive to the modeled radiant heat and firebrand-proxy entry mechanisms, whose removal most degrades performance. WUIRL provides a foundation for future studies of structure loss under dynamic exposure, offering the potential to better test specific interventions' role in limiting future damage as higher resolution observations become available.