Reinforcement Learning with a Lost Person Model for Search and Rescue Path Planning
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Date
2025-05-12
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Virginia Tech
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.
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Keywords
Reinforcement Learning, Search and Rescue, Path Planning