Modeling and controlling fluid flows is critical for several fields of science and engineering, including transportation, energy, and medicine. Effective flow control can lead to, e.g., lift increase, drag reduction, mixing enhancement, and noise reduction. However, controlling a fluid faces several significant challenges, including high-dimensional, nonlinear, and multiscale interactions in space and time. Reinforcement learning (RL) has recently shown great success in complex domains, such as robotics and protein folding, but its application to flow control is hindered by a lack of standardized benchmark platforms and the computational demands of fluid simulations. To address these challenges, we introduce HydroGym, a solver-independent RL platform for flow control research. HydroGym integrates sophisticated flow control benchmarks, scalable runtime infrastructure, and state-of-the-art RL algorithms. Our platform includes 42 validated environments spanning from canonical laminar flows to complex three-dimensional turbulent scenarios, validated over a wide range of Reynolds numbers. We provide non-differentiable solvers for traditional RL and differentiable solvers that dramatically improve sample efficiency through gradient-enhanced optimization. Comprehensive evaluation reveals that RL agents consistently discover robust control principles across configurations, such as boundary layer manipulation, acoustic feedback disruption, and wake reorganization. Transfer learning studies demonstrate that controllers learned at one Reynolds number or geometry adapt efficiently to new conditions, requiring approximately 50% fewer training episodes. The HydroGym platform is highly extensible and scalable, providing a framework for researchers in fluid dynamics, machine learning, and control to add environments, surrogate models, and control algorithms to advance science and technology.

翻译：建模与控制流体流动对于科学与工程的多个领域至关重要，涵盖交通、能源与医学等。有效的流动控制可带来诸如升力提升、阻力降低、混合增强及噪声抑制等效益。然而，流体控制面临若干重大挑战，包括空间与时间上的高维、非线性及多尺度相互作用。强化学习（RL）近年来在机器人学、蛋白质折叠等复杂领域取得显著成功，但其在流动控制中的应用因缺乏标准化基准平台及流体模拟的高计算需求而受到阻碍。为应对这些挑战，我们推出 HydroGym——一个独立于求解器的流动控制研究强化学习平台。HydroGym 集成了先进的流动控制基准、可扩展的运行时基础设施以及前沿的强化学习算法。我们的平台包含 42 个经过验证的环境，涵盖从经典层流到复杂三维湍流场景，并在广泛的雷诺数范围内进行了验证。我们提供适用于传统强化学习的不可微求解器，以及通过梯度增强优化显著提升样本效率的可微求解器。综合评估表明，强化学习智能体在不同构型中持续发现鲁棒的控制原理，例如边界层调控、声学反馈抑制与尾流重组。迁移学习研究表明，在某一雷诺数或几何构型下习得的控制器能高效适应新条件，所需训练回合数减少约 50%。HydroGym 平台具备高度可扩展性，为流体动力学、机器学习与控制领域的研究者提供了添加环境、代理模型与控制算法的框架，以推动科学与技术进步。