| 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}, | |
| } | |
| ``` |