  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.
