Explainable Causal Reinforcement Learning for wildfire evacuation logistics networks with inverse simulation verification
The Spark: A Learning Journey into Crisis AI
It started with a simulation that refused to behave. I was tinkering with a simple evacuation model—a grid of roads, a spreading fire, and a few thousand virtual agents trying to escape. The reinforcement learning agent I'd trained kept sending evacuees toward the fire. Not because it was broken, but because my reward function had inadvertently rewarded "movement" over "survival."
That failure was my gateway into a deeper question: how do we build AI systems that not only optimize complex logistics under extreme uncertainty but also explain why they make those decisions? And how do we verify those explanations when we can't run real-world experiments?
This article chronicles my exploration of Explainable Causal Reinforcement Learning (XC-RL) applied to the nightmarish complexity of wildfire evacuation logistics—and the inverse simulation framework I built to verify what the models claim to have learned.
