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.

翻译：强化学习在主动流动控制领域已展现出良好前景，但该领域的进展仍难以评估，因为现有研究依赖于异构的观测与执行方案、数值设置及评估协议。当前流动控制基准测试尝试解决这些问题，但严重依赖外部计算流体动力学求解器，不具备完全可微性，且对三维与多智能体场景的支持有限。为突破这些限制，我们提出了首个面向流动控制强化学习的独立、完全可微基准测试套件FluidGym。该框架完全基于PyTorch构建于GPU加速的PICT求解器之上，在单一Python栈中运行，无需外部计算流体动力学软件，并提供标准化评估协议。我们展示了PPO与SAC算法的基线结果，并将所有环境、数据集及训练模型作为公共资源发布。FluidGym实现了控制方法的系统性比较，为未来基于学习的流动控制研究建立了可扩展的基础，项目地址为https://github.com/safe-autonomous-systems/fluidgym。