Encoding the Sensor Allocation Problem for Reinforcement Learning

Traditionally, space situational awareness (SSA) sensor networks have relied on dynamic programming theory to generate tasking plans which govern how sensors are allocated to observe resident space objects. Deep reinforcement learning (DRL) techniques, with their ability to be trained on simulated environments, which are readily available for the SSA sensor allocation problem, and demonstrated performance in other fields, have potential to exceed performance of deterministic methods. The research presented in this dissertation develops techniques for encoding an SSA environment model to apply DRL to the sensor allocation problem. This dissertation is the compilation of two separate but related studies. The first study compares two alternative invalid action handling techniques, penalization and masking. The second study examines the performance of policies that have forecast state knowledge incorporated in the observation space.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Aerospace Engineering

Department dc:contributor.department

Aerospace and Ocean Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Penn, Dylan R.

Chair dc:contributor.committeechair

• Black, Jonathan T.

Committee members dc:contributor.committeemember

• Schroeder, Kevin Kent
• Smith, Leonard Allen
• Fowler, Michael Chrispatrick

• space traffic management
• resource allocation
• 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:40264

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/119007