Howell, Bryson L.2025-06-132025-06-132025-05-12https://hdl.handle.net/10919/135507In this thesis, we train a reinforcement learning agent to plan paths for search and rescue applications using a model of lost person behavior trained on past search incidents. We propose an improved method for producing occupancy maps from the trajectories of an agent-based lost person model. We demonstrate that through an end-to-end learning approach our agent can generalize to novel search incidents without directly observing the probability distribution describing search risk.ETDapplication/pdfenIn CopyrightReinforcement LearningSearch and RescuePath PlanningThesis