The official implementation of "Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization". OMIGA provides a principled framework to convert global-level value regularization into equivalent implicit local value regularizations and simultaneously enables in-sample learning, thus elegantly bridging multi-agent value decomposition and policy learning with offline regularizations. This repository is inspired by the TRPO-in-MARL library for online Multi-Agent RL.

This repo provides the implementation of OMIGA in Multi-agent MuJoCo.

conda create -n env_name python=3.9
conda activate OMIGA
git clone https://github.com/ZhengYinan-AIR/OMIGA.git
cd OMIGA
pip install -r requirements.txt

Before running the code, you need to download the necessary offline datasets (Download link). Then, make sure the config file at configs/config.py is correct. Set the data_dir parameter as the storage location for the downloaded data, and configure parameters scenario, agent_conf, and data_type. You can run the code as follows:

# If the location of the dataset is at: "/data/Ant-v2-2x4-expert.hdf5"
cd OMIGA
python run_mujoco.py --data_dir="/data/" --scenario="Ant-v2" --agent_conf="2x4" --data_type="expert"

This codebase can also log to W&B online visualization platform. To log to W&B, you first need to set your W&B API key environment variable:

wandb online
export WANDB_API_KEY='YOUR W&B API KEY HERE'

Then you can run experiments with W&B logging turned on:

python run_mujoco.py --wandb=True

If you find our code and paper can help, please cite our paper as:

@article{wang2023offline,
  title={Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization},
  author={Wang, Xiangsen and Xu, Haoran and Zheng, Yinan and Zhan, Xianyuan},
  journal={Advances in Neural Information Processing Systems},
  year={2023}
}