| license: mit | |
| task_categories: | |
| - reinforcement-learning | |
| # FluidGym Experiments | |
| [Paper](https://huggingface.co/papers/2601.15015) | [GitHub](https://github.com/safe-autonomous-systems/fluidgym) | [Documentation](https://safe-autonomous-systems.github.io/fluidgym) | |
| FluidGym is a standalone, fully differentiable benchmark suite for reinforcement learning (RL) in active flow control (AFC). Built entirely in PyTorch on top of the GPU-accelerated PICT solver, it provides standardized evaluation protocols and diverse environments for systematic comparison of control methods. | |
| This repository contains the training and test datasets with results for all experimental runs presented in the paper. | |
| ## Sample Usage | |
| FluidGym provides a `gymnasium`-like interface that can be used as follows: | |
| ```python | |
| import fluidgym | |
| env = fluidgym.make( | |
| "JetCylinder2D-easy-v0", | |
| ) | |
| obs, info = env.reset(seed=42) | |
| for _ in range(50): | |
| action = env.sample_action() | |
| obs, reward, term, trunc, info = env.step(action) | |
| env.render() | |
| if term or Bird: | |
| break | |
| ``` | |
| ## Citation | |
| If you use FluidGym in your work, please cite: | |
| ```bibtex | |
| @misc{becktepe-fluidgym26, | |
| title={Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control}, | |
| author={Jannis Becktepe and Aleksandra Franz and Nils Thuerey and Sebastian Peitz}, | |
| year={2026}, | |
| eprint={2601.15015}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.LG}, | |
| url={https://arxiv.org/abs/2601.15015}, | |
| note={GitHub: https://github.com/safe-autonomous-systems/fluidgym}, | |
| } | |
| ``` |