A Reinforcement Learning-based Scheduler for Minimizing Casualties of a Military Drone Swarm

In this thesis, we consider a swarm of military drones flying over an unfriendly territory, where a drone can be shot down by an enemy with an age-based risk probability. We study the problem of scheduling surveillance image transmissions among the drones with the objective of minimizing the overall casualty. We present Hector, a reinforcement learning-based scheduling algorithm. Specifically, Hector only uses the age of each detected target, a piece of locally available information at each drone, as an input to a neural network to make scheduling decisions. Extensive simulations show that Hector significantly reduces casualties than a baseline round-robin algorithm. Further, Hector can offer comparable performance to a high-performing greedy scheduler, which assumes complete knowledge of global information.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science and Applications

Department dc:contributor.department

Computer Science

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2022

Author dc:creator

• Jin, Heng

Chair dc:contributor.committeechair

• Hou, Yiwei Thomas

Committee members dc:contributor.committeemember

• Lou, Wenjing
• Liu, Qingyu

• Drone swarm
• Casualty
• Scheduling
• Age of Information
• Reinforcement learning

Statement dc:rights

• In Copyright

Licence dc:rights.uri

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

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:35276

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/111255