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Reinforcement learning for telescope optimisation
Reinforcement learning is a relatively new and unexplored branch of machine learning with a wide variety of applications. This study investigates reinforcement learning and provides an overview of its application to a variety of different problems. We then explore the possible use of reinforcement l...
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
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Department of Statistical Sciences
2020
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| _version_ | 1867613646738161664 |
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| access_status_str | Open Access |
| author | Blows, Curtly |
| author2 | Bassett, Bruce |
| author_browse | Bassett, Bruce Blows, Curtly |
| author_facet | Bassett, Bruce Blows, Curtly |
| author_sort | Blows, Curtly |
| collection | Thesis |
| description | Reinforcement learning is a relatively new and unexplored branch of machine learning with a wide variety of applications. This study investigates reinforcement learning and provides an overview of its application to a variety of different problems. We then explore the possible use of reinforcement learning for telescope target selection and scheduling in astronomy with the hope of effectively mimicking the choices made by professional astronomers. This is relevant as next-generation astronomy surveys will require near realtime decision making in response to high-speed transient discoveries. We experiment with and apply some of the leading approaches in reinforcement learning to simplified models of the target selection problem. We find that the methods used in this study show promise but do not generalise well. Hence while there are indications that reinforcement learning algorithms could work, more sophisticated algorithms and simulations are needed. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/31352 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:39:27.863Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/31352 Reinforcement learning for telescope optimisation Blows, Curtly Bassett, Bruce Statistical Sciences Reinforcement learning is a relatively new and unexplored branch of machine learning with a wide variety of applications. This study investigates reinforcement learning and provides an overview of its application to a variety of different problems. We then explore the possible use of reinforcement learning for telescope target selection and scheduling in astronomy with the hope of effectively mimicking the choices made by professional astronomers. This is relevant as next-generation astronomy surveys will require near realtime decision making in response to high-speed transient discoveries. We experiment with and apply some of the leading approaches in reinforcement learning to simplified models of the target selection problem. We find that the methods used in this study show promise but do not generalise well. Hence while there are indications that reinforcement learning algorithms could work, more sophisticated algorithms and simulations are needed. 2020-02-27T13:28:16Z 2020-02-27T13:28:16Z 2019 2020-02-27T11:35:34Z Master Thesis Masters MSc http://hdl.handle.net/11427/31352 eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Statistical Sciences Blows, Curtly Reinforcement learning for telescope optimisation |
| thesis_degree_str | Master's |
| title | Reinforcement learning for telescope optimisation |
| title_full | Reinforcement learning for telescope optimisation |
| title_fullStr | Reinforcement learning for telescope optimisation |
| title_full_unstemmed | Reinforcement learning for telescope optimisation |
| title_short | Reinforcement learning for telescope optimisation |
| title_sort | reinforcement learning for telescope optimisation |
| topic | Statistical Sciences |
| url | http://hdl.handle.net/11427/31352 |
| work_keys_str_mv | AT blowscurtly reinforcementlearningfortelescopeoptimisation |