HydroGym: A Reinforcement Learning Platform for Fluid Dynamics

Abstract

The modeling and control of fluid flows remain a significant challenge with tremendous potential to advance fields including transportation, energy, and medicine. Effective fluid flow control can lead to drag reduction, enhanced mixing, and noise reduction, among other applications. While reinforcement learning (RL) has shown great success in complex domains, such as robotics and protein folding, its application to flow control is hindered by the lack of standardized 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, a scalable runtime, and state-of-the-art RL algorithms. Our platform includes four validated non-differentiable fluid flow environments and one differentiable environment, all evaluated with a variety of modern RL algorithms. HydroGym’s scalable design allows computations to run seamlessly from laptops to high-performance computing resources, providing a standardized interface for implementing new flow environments. HydroGym aims to bridge the gap in flow control research, providing a robust platform to support both non-differentiable and differentiable RL techniques, fostering advancements in scientific machine learning.