Introduction

HydroGym is a reinforcement learning platform for active flow control in fluid dynamics. It provides a unified, Gymnasium-compatible interface to 88 pre-configured CFD environments that span six solver backends, two to three spatial dimensions, and Reynolds numbers from laminar benchmarks to wall-bounded turbulence above Re = 400 000.

The central design goal is to make it straightforward to apply standard RL tooling and techniques — Stable-Baselines3, TorchRL, RLlib, PettingZoo, or your own training loop — to a broad set of physically meaningful flow control problems without having to write a custom environment or solver interface. Every environment exposes the same env.reset() / env.step() API; solver-specific setup is handled behind the scenes.

Environment configurations and initial-condition checkpoints are distributed through HuggingFace Hub as a dedicated dataset and are downloaded automatically the first time an environment is created. Pre-built Docker images are available for all solver backends, making it possible to go from zero to a running training loop in a single docker pull command.

The six presently supported solver backends are:

Backend	Method	Environments	Dimensions
Firedrake	Finite element (FEM)	20	2-D
MAIA LBM	Lattice Boltzmann	55	2-D, 3-D
MAIA Structured FV	Finite volume	8	3-D
NEK5000	Spectral element (SEM)	1	3-D
JAX	Spectral / finite-difference, fully differentiable	2	2-D, 3-D
JAX-Fluids	Compressible finite volume, fully differentiable	2	2-D, 3-D

To get a solver running immediately, proceed to the Quickstart.