Reinforcement Learning with a Lost Person Model for Search and Rescue Path Planning

Abstract

In 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.