Reinforcement learning

TL;DR

This paper reviews how reinforcement learning can automate and improve various operational tasks in astronomy, such as planning and data collection, by teaching AI agents to perform these complex, tedious activities.

Contribution

It provides a comprehensive overview of reinforcement learning techniques and discusses their potential applications and benefits in the field of astronomy.

Findings

Reinforcement learning can automate astronomical data collection tasks.

It has the potential to optimize scheduling and resource allocation in observatories.

Reinforcement learning methods can enhance the efficiency of astronomical research workflows.

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.