Implementation of Deep Reinforcement Learning for Radio Telescope Control and Scheduling
| dc.contributor.author | Puangragsa, Sarut | |
| dc.contributor.author | Sahavisit, Tanawit | |
| dc.contributor.author | Laon, Popphon | |
| dc.contributor.author | Puangragsa, Utumporn | |
| dc.contributor.author | Phasukkit, Pattarapong | |
| dc.date.accessioned | 2026-08-06T10:52:47Z | |
| dc.date.available | 2026-08-06T10:52:47Z | |
| dc.date.issued | 2025-12-01 | |
| dc.description.abstract | The proliferation of terrestrial and space-based communication systems introduces significant radio frequency interference (RFI), which severely compromises data acquisition for radio telescopes, necessitating robust and dynamic scheduling solutions. This study addresses this challenge by implementing a Deep Recurrent Reinforcement Learning (DRL) framework for the control and dynamic scheduling of the X-Y pedestal-mounted KMITL radio telescope, explicitly trained for RFI avoidance. The methodology involved developing a custom simulation environment with a domain-specific Convolutional Neural Network (CNN) feature extractor and a Long Short-Term Memory (LSTM) network to model temporal dynamics and long-horizon planning. Comparative evaluation demonstrated that the recurrent DRL agent achieved a mean effective survey coverage of 475 deg<sup>2</sup>/h, representing a 72.7% superiority over the non-recurrent baseline, and maintained exceptional stability with only 1.0% degradation in median coverage during real-world deployment. The DRL framework offers a highly reliable and adaptive solution for telescope scheduling that is capable of maintaining survey efficiency while proactively managing dynamic RFI sources. | |
| dc.identifier.citation | Galaxies, 13(6), 2025 | |
| dc.identifier.doi | 10.3390/galaxies13060137 | |
| dc.identifier.issn | 20754434 | |
| dc.identifier.other | 2-s2.0-105026186938 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/17397 | |
| dc.source | Galaxies | |
| dc.subject | deep reinforcement learning | |
| dc.subject | radio astronomy | |
| dc.subject | telescope control system | |
| dc.title | Implementation of Deep Reinforcement Learning for Radio Telescope Control and Scheduling | |
| dc.type | Article |