| Abstrakt: |
In the past couple of years, the interest of the fluid mechanics community for deep reinforcement learning techniques has increased at fast pace, leading to a growing bibliography on the topic. Due to its ability to solve complex decision-making problems, deep reinforcement learning has especially emerged as a valuable tool to perform flow control, but recent publications also advertise the great potential for other applications, such as shape optimization or microfluidics. The present work proposes an exhaustive review of the existing literature and is a follow-up to our previous review on the topic. The contributions are regrouped by the domain of application and are compared together regarding algorithmic and technical choices, such as state selection, reward design, time granularity, and more. Based on these comparisons, general conclusions are drawn regarding the current state-of-the-art, and perspectives for future improvements are sketched. [ABSTRACT FROM AUTHOR] |
