CarloRomeoHugging/ARC_RL

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
• GitHub Repository: 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:

python main.py --algo sacfd --env queen --log-wandb

To regenerate or collect expert episodes manually:

python -m src.utils.collect_dataset --env bastion --n-episodes 1000

Citation

@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}, 
}