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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},
}
``` |