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

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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2025

Author dc:creator

• Howell, Bryson L.

Chair dc:contributor.committeechair

• Williams, Ryan K.

Committee members dc:contributor.committeemember

• Lau, Nathan
• Doan, Thinh T.

• Reinforcement Learning
• Search and Rescue
• Path Planning

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10919/135507

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/135507