Explainable Causal Reinforcement Learning for wildfire evacuation logistics networks in carbon-negative infrastructure

This research focuses on overcoming the limitations of standard Reinforcement Learning models in optimizing wildfire evacuations. The author applies causal inference, inspired by Judea Pearl and Bernhard Schölkopf, to address inexplicable recommendations and confounding variables.