Recently, the increasing use of deep reinforcement learning for flow control problems has led to a new area of research, focused on the coupling and the adaptation of the existing algorithms to the control of numerical fluid dynamics environments. Although still in its infancy, the field has seen multiple successes in a short time span, and its fast development pace can certainly be partly imparted to the open-source effort that drives the expansion of the community. Yet, this emerging domain still misses a common ground to (i) ensure the reproducibility of the results, and (ii) offer a proper ad-hoc benchmarking basis. To this end, we propose Beacon, an open-source benchmark library composed of seven lightweight 1D and 2D flow control problems with various characteristics, action and observation space characteristics, and CPU requirements. In this contribution, the seven considered problems are described, and reference control solutions are provided. The sources for the following work are available at https://github.com/jviquerat/beacon.

翻译：近年来，深度强化学习在流动控制问题中的广泛应用催生了新的研究领域，重点关注现有算法与数值流体动力学环境控制的耦合与适配。尽管该领域仍处于起步阶段，但短期内已取得多项成功，其快速发展在一定程度上归功于推动社区扩张的开源努力。然而，这一新兴领域仍缺乏共同基础，以（i）确保结果的可复现性，并（ii）提供适当的专用基准测试平台。为此，我们提出Beacon——一个由七个轻量级一维和二维流动控制问题组成的开源基准库，涵盖不同特性、动作与观测空间特征及CPU需求。本文对上述七个问题进行了描述，并提供了参考控制解决方案。本工作的源代码见https://github.com/jviquerat/beacon。