Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control

Reinforcement learning ({RL}) has shown promising results in active flow control ({AFC}), yet progress in the field remains difficult to assess as existing studies rely on heterogeneous observation and actuation schemes, numerical setups, and evaluation protocols. Current {AFC} benchmarks attempt to address these issues but heavily rely on external computational fluid dynamics ({CFD}) solvers, are not fully differentiable, and provide limited 3D and multi-agent support. To overcome these limitations, we introduce {FluidGym}, the first standalone, fully differentiable benchmark suite for {RL} in {AFC}. Built entirely in {PyTorch} on top of the {GPU}-accelerated {PICT} solver, {FluidGym} runs in a single Python stack, requires no external {CFD} software, and provides standardized evaluation protocols. We present baseline results with {PPO} and {SAC} and release all environments, datasets, and trained models as public resources. {FluidGym} enables systematic comparison of control methods, establishes a scalable foundation for future research in learning-based flow control, and is available at https://github.com/safe-autonomous-systems/fluidgym.