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}
}
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.txtcd collaborative_team_tasks
python train.pycd mix_colla_compe_team_tasks
python train.pyThe 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.
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