The paper is now available in arXiv and accepted by NeurIPS 2021. Our approach can help both value-based and policy-based baselines (such as QMIX, QPLEX, and MAPPO) to explore sophisticated strategies for improving learning efficiency in challenging benchmarks.

This codebase accompanies the paper submission "Celebrating Diversity in Shared Multi-Agent Reinforcement Learning"(CDS website) and is based on GRF, PyMARL and SMAC codebases which are open-sourced.

If you find this repository useful, please cite our paper:

@article{chenghao2021celebrating,
  title={Celebrating diversity in shared multi-agent reinforcement learning},
  author={Li, Chenghao, and Wang, Tonghan and Wu, Chengjie and Zhao, Qianchuan and Yang, Jun and Zhang, Chongjie},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}