Reinforcement learning
This work is incremental, as it applies an existing method (reinforcement learning) to a new domain (astronomy) without introducing novel techniques.
The paper addresses the tedious operational tasks in astronomy, such as planning and data processing, by proposing the use of reinforcement learning to automate these processes, though no concrete results or numbers are provided.
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.