Add dataset card and documentation for ARC-RL

Hi, I'm Niels from the Hugging Face community science team. I noticed this dataset didn't have a dataset card yet. This PR adds a card with metadata (task categories and license), links to the research paper and GitHub repository, a description of the robotic morphologies, sample usage instructions, and the BibTeX citation.

README.md

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+---
+license: mit
+task_categories:
+- reinforcement-learning
+tags:
+- robotics
+- mujoco
+- continuous-control
+---
+
+# ARC-RL: A Reinforcement Learning Playground Inspired by ARC Raiders
+
+This repository contains the expert demonstration datasets for **ARC-RL**, a suite of four MuJoCo continuous-control environments featuring robotic morphologies inspired by the game *ARC Raiders*.
+
+- **Paper:** [ARC-RL: A Reinforcement Learning Playground Inspired by ARC Raiders](https://huggingface.co/papers/2605.19503)
+- **GitHub Repository:** [https://github.com/CarloRomeo427/ARC_RL](https://github.com/CarloRomeo427/ARC_RL)
+
+## Robot Morphologies
+The benchmark features four unique robotic body plans:
+- 👑 **Queen**: 18-DoF hexapod.
+- 🛡️ **Bastion**: 12-DoF armoured hexapod.
+- 🐸 **Leaper**: 12-DoF quadruped.
+- 🪲 **Tick**: 18-DoF compact hexapod.
+
+The datasets provided here are hand-crafted Central Pattern Generator (CPG) demonstrations for each morphology, which serve as fixed expert references and as sources of prior data for offline-to-online reinforcement learning algorithms like SACfD, SPEQ-O2O, and SOPE.
+
+## Usage
+
+To launch training with prior data (which automatically downloads the necessary files from this repository), use an algorithm like `sacfd` or `sope`:
+
+```bash
+python main.py --algo sacfd --env queen --log-wandb
+```
+
+To regenerate or collect expert episodes manually:
+
+```bash
+python -m src.utils.collect_dataset --env bastion --n-episodes 1000
+```
+
+## Citation
+
+```bibtex
+@misc{romeo2026arcrlreinforcementlearningplayground,
+      title={ARC-RL: A Reinforcement Learning Playground Inspired by ARC Raiders}, 
+      author={Carlo Romeo and Andrew D. Bagdanov},
+      year={2026},
+      eprint={2605.19503},
+      archivePrefix={arXiv},
+      primaryClass={cs.RO},
+      url={https://arxiv.org/abs/2605.19503}, 
+}
+```