  We propose an open-source python platform for applications of Deep
Reinforcement Learning (DRL) in fluid mechanics. DRL has been widely used in
optimizing decision-making in nonlinear and high-dimensional problems. Here, an
agent maximizes a cumulative reward with learning a feedback policy by acting
in an environment. In control theory terms, the cumulative reward would
correspond to the cost function, the agent to the actuator, the environment to
the measured signals and the learned policy to the feedback law. Thus, DRL
assumes an interactive environment or, equivalently, control plant. The setup
of a numerical simulation plant with DRL is challenging and time-consuming. In
this work, a novel python platform, named DRLinFluids is developed for this
purpose, with DRL for flow control and optimization problems in fluid
mechanics. The simulations employ OpenFOAM as popular, flexible Navier-Stokes
solver in industry and academia, and Tensorforce or Tianshou as widely used
versatile DRL packages. The reliability and efficiency of DRLinFluids are
demonstrated for two wake stabilization benchmark problems. DRLinFluids
significantly reduces the application effort of DRL in fluid mechanics and is
expected to greatly accelerates academic and industrial applications.
