Subequivariant Reinforcement Learning in 3D Multi-Entity Physical Environments

ICML 2024

[Project Page] [Paper]

Citation

If you use this codebase for your research, please cite the paper:

@inproceedings{chen2024smerl,
    title = {Subequivariant Reinforcement Learning in 3D Multi-Entity Physical Environments},
    author = {Chen, Runfa and Wang, ling and Du, Yu and Xue, Tianrui and Sun, Fuchun and Zhang, Jianwei and Huang, Wenbing},
    booktitle={International Conference on Machine Learning},
    year={2024},
    organization={PMLR}
}

Installation

conda create -n jax python=3.10
conda activate jax
pip install --upgrade pip
pip install jax[cuda11_pip]==0.4.14 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html  
pip install -r requirements.txt

Team Reach

cd collaborative_team_tasks
python train.py

Team Sumo

cd mix_colla_compe_team_tasks
python train.py

Acknowledgement

The MARL code is based on Brax and the morphology-based implementation is built on top of MxT Bench (Furuta et al., ICLR 2023), SGRL (Chen et al., ICML 2023 Oral)repository.