Reinforcement learning
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917668346396672 |
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| author | Yatawatta, Sarod |
| author_facet | Yatawatta, Sarod |
| contents | Observing celestial objects and advancing our scientific knowledge about them involves tedious planning, scheduling, data collection and data post-processing. Many of these operational aspects of astronomy are guided and executed by expert astronomers. Reinforcement learning is a mechanism where we (as humans and astronomers) can teach agents of artificial intelligence to perform some of these tedious tasks. In this paper, we will present a state of the art overview of reinforcement learning and how it can benefit astronomy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10369 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reinforcement learning Yatawatta, Sarod Instrumentation and Methods for Astrophysics Artificial Intelligence Machine Learning Observing celestial objects and advancing our scientific knowledge about them involves tedious planning, scheduling, data collection and data post-processing. Many of these operational aspects of astronomy are guided and executed by expert astronomers. Reinforcement learning is a mechanism where we (as humans and astronomers) can teach agents of artificial intelligence to perform some of these tedious tasks. In this paper, we will present a state of the art overview of reinforcement learning and how it can benefit astronomy. |
| title | Reinforcement learning |
| topic | Instrumentation and Methods for Astrophysics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2405.10369 |