  Deep Reinforcement Learning (DRL) has recently been proposed as a methodology
to discover complex Active Flow Control (AFC) strategies [Rabault, J., Kuchta,
M., Jensen, A., Reglade, U., & Cerardi, N. (2019): "Artificial neural networks
trained through deep reinforcement learning discover control strategies for
active flow control", Journal of Fluid Mechanics, 865, 281-302]. However, while
promising results were obtained on a simple 2D benchmark flow at a moderate
Reynolds number, considerable speedups will be required to investigate more
challenging flow configurations. In the case of DRL trained with Computational
Fluid Dynamics (CFD) data, it was found that the CFD part, rather than training
the Artificial Neural Network, was the limiting factor for speed of execution.
Therefore, speedups should be obtained through a combination of two approaches.
The first one, which is well documented in the literature, is to parallelize
the numerical simulation itself. The second one is to adapt the DRL algorithm
for parallelization. Here, a simple strategy is to use several independent
simulations running in parallel to collect experiences faster. In the present
work, we discuss this solution for parallelization. We illustrate that perfect
speedups can be obtained up to the batch size of the DRL agent, and slightly
suboptimal scaling still takes place for an even larger number of simulations.
This is, therefore, an important step towards enabling the study of more
sophisticated Fluid Mechanics problems through DRL.
