Reinforcement learning
- URL: http://arxiv.org/abs/2405.10369v1
- Date: Thu, 16 May 2024 18:03:17 GMT
- Title: Reinforcement learning
- Authors: Sarod Yatawatta,
- Abstract summary: 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.
- Score: 0.8702432681310399
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Aurora: A Foundation Model of the Atmosphere [56.97266186291677]
We introduce Aurora, a large-scale foundation model of the atmosphere trained on over a million hours of diverse weather and climate data.
In under a minute, Aurora produces 5-day global air pollution predictions and 10-day high-resolution weather forecasts.
arXiv Detail & Related papers (2024-05-20T14:45:18Z) - Large Language Models for Scientific Synthesis, Inference and
Explanation [56.41963802804953]
We show how large language models can perform scientific synthesis, inference, and explanation.
We show that the large language model can augment this "knowledge" by synthesizing from the scientific literature.
This approach has the further advantage that the large language model can explain the machine learning system's predictions.
arXiv Detail & Related papers (2023-10-12T02:17:59Z) - Artificial Intelligence for Science in Quantum, Atomistic, and Continuum
Systems [245.1050780515017]
New area of research known as AI for science (AI4Science)
Areas aim at understanding the physical world from subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales.
Key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods.
arXiv Detail & Related papers (2023-07-17T12:14:14Z) - Assessing Exoplanet Habitability through Data-driven Approaches: A
Comprehensive Literature Review [0.0]
Review aims to illuminate the emerging trends and advancements within exoplanet research.
Focuses on interplay between exoplanet detection, classification, and visualization.
Describes the broad spectrum of machine learning approaches employed in exoplanet research.
arXiv Detail & Related papers (2023-05-18T17:18:15Z) - Artificial intelligence to advance Earth observation: a perspective [56.13510552915079]
This article gives a bird's eye view of the essential scientific tools and approaches informing and supporting the transition from raw EO data to usable EO-based information.
We cover the impact of (i) Computer vision; (ii) Machine learning; (iii) Advanced processing and computing; (iv) Knowledge-based AI; (v) Explainable AI and causal inference; (vi) Physics-aware models; (vii) User-centric approaches; and (viii) the much-needed discussion of ethical and societal issues related to the massive use of ML technologies in EO.
arXiv Detail & Related papers (2023-05-15T07:47:24Z) - Applications of AI in Astronomy [0.0]
We provide an overview of the use of Machine Learning (ML) and other AI methods in astronomy, astrophysics, and cosmology.
Over the past decade we have seen an exponential growth of the astronomical literature involving a variety of ML/AI applications.
As the data complexity continues to increase, we anticipate further advances leading towards a collaborative human-AI discovery.
arXiv Detail & Related papers (2022-12-03T00:38:59Z) - Elements of effective machine learning datasets in astronomy [1.552171919003135]
We identify elements of effective machine learning datasets in astronomy.
We discuss why these elements are important for astronomical applications and ways to put them in practice.
arXiv Detail & Related papers (2022-11-25T23:37:24Z) - Astronomia ex machina: a history, primer, and outlook on neural networks
in astronomy [0.0]
We trace the evolution of connectionism in astronomy through its three waves.
We argue for the adoption of GPT-like foundation models fine-tuned for astronomical applications.
arXiv Detail & Related papers (2022-11-07T19:00:00Z) - A Survey of Exploration Methods in Reinforcement Learning [64.01676570654234]
Reinforcement learning agents depend crucially on exploration to obtain informative data for the learning process.
In this article, we provide a survey of modern exploration methods in (Sequential) reinforcement learning, as well as a taxonomy of exploration methods.
arXiv Detail & Related papers (2021-09-01T02:36:14Z) - Actionable Models: Unsupervised Offline Reinforcement Learning of
Robotic Skills [93.12417203541948]
We propose the objective of learning a functional understanding of the environment by learning to reach any goal state in a given dataset.
We find that our method can operate on high-dimensional camera images and learn a variety of skills on real robots that generalize to previously unseen scenes and objects.
arXiv Detail & Related papers (2021-04-15T20:10:11Z) - Self-supervised Learning for Astronomical Image Classification [1.2891210250935146]
In Astronomy, a huge amount of image data is generated daily by photometric surveys.
We propose a technique to leverage unlabeled astronomical images to pre-train deep convolutional neural networks.
arXiv Detail & Related papers (2020-04-23T17:32:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.