Add dataset card with paper link, task category, and sample usage

README.md

@@ -1,3 +1,51 @@
----
-license: mit
----
+---
+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}, 
+}
+```