Encoding the Sensor Allocation Problem for Reinforcement Learning
| dc.contributor.author | Penn, Dylan R. | en |
| dc.contributor.committeechair | Black, Jonathan T. | en |
| dc.contributor.committeemember | Schroeder, Kevin Kent | en |
| dc.contributor.committeemember | Smith, Leonard Allen | en |
| dc.contributor.committeemember | Fowler, Michael Chrispatrick | en |
| dc.contributor.department | Aerospace and Ocean Engineering | en |
| dc.date.accessioned | 2024-05-17T08:00:45Z | en |
| dc.date.available | 2024-05-17T08:00:45Z | en |
| dc.date.issued | 2024-05-16 | en |
| dc.description.abstract | 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. | en |
| dc.description.abstractgeneral | Resident space objects (RSOs) are typically tracked by ground-based sensors (telescopes and radar). Determining how to allocate sensors to RSOs is a complex problem traditionally performed by dynamic programming techniques. Deep reinforcement learning (DRL), a subset of machine learning, has demonstrated performance in other fields, and has the potential to exceed performance of traditional techniques. The research presented in this dissertation develops techniques for encoding a space situational awareness 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. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:40264 | en |
| dc.identifier.uri | https://hdl.handle.net/10919/119007 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | space traffic management | en |
| dc.subject | resource allocation | en |
| dc.subject | reinforcement learning | en |
| dc.title | Encoding the Sensor Allocation Problem for Reinforcement Learning | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Aerospace Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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