This is the code for Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning. We also provide well-organized implementations of curriculum MARL algorithms based on Ray 2.0:
| Task Generator | Paper |
|---|---|
| SPC | Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning |
| VACL | Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems |
| ALP-GMM | Absolute Learning Progress and Gaussian Mixture Models for Automatic Curriculum Learning |
| Uniform | / |
| Non-curriculum (IPPO) | / |
Docker is recommended.
To install:
docker build --build-arg USER_UID=$(id -u) --build-arg USER_GID=$(id -g) -t spc -f Dockerfile .To run experiments with detached mode:
./run_experiments.shIf you want to run in interactive mode, change the -d parameter to -it.
Configurations can be found in the configs/ directory.
sudo apt-get install git cmake build-essential libgl1-mesa-dev libsdl2-dev \
libsdl2-image-dev libsdl2-ttf-dev libsdl2-gfx-dev libboost-all-dev \
libdirectfb-dev libst-dev mesa-utils xvfb x11vnc python3-pip
conda create -n spc python=3.8
conda activate spc
conda install -c anaconda libffi
python -m pip install ray[rllib]==2.0.1
python -m pip install -r requirements.txt
python -m pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.htmlTraining Examples:
python train.py -f configs/football/ppo/corner.yamlPlease cite our paper if you've found this repository useful:
@article{wang2023towards,
title={Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning},
author={Wang, Rundong and Zheng, Longtao and Qiu, Wei and He, Bowei and An, Bo and Rabinovich, Zinovi and Hu, Yujing and Chen, Yingfeng and Lv, Tangjie and Fan, Changjie},
journal={arXiv preprint arXiv:2302.03429},
year={2023}
}