GraphMIX is a multi-agent deep reinforcement learning (MARL) approach that relies on a graph neural network (GNN) architecture for combining the individual agent value functions into a global team value funtion, and it provides state-of-the-art results across several maps in the StarCraft Multi-Agent Challenge (SMAC) benchmark.

This repository contains the PyTorch-based implementation of GraphMIX and relies on the PyMARL and SMAC libraries.

Build the Dockerfile using

cd docker
bash build.sh

Set up StarCraft II and SMAC:

bash install_sc2.sh

This will download SC2 into the 3rdparty folder and copy the maps necessary to run over.

The requirements.txt file can be used to install the necessary packages into a virtual environment (not recomended).

Use the following command to run GraphMIX on any desired SMAC map (e.g., corridor in the example below):

python3 src/main.py --config=graphmix --env-config=sc2 with \
env_args.map_name=corridor lambda_local=1 test_interval=20000 test_nepisode=32 \
t_max=5000000 epsilon_anneal_time=500000

The parameter lambda_local represents the local loss coefficient in the GraphMIX objective function.

All results will be stored in the results folder.

You can save the learnt models to disk by setting save_model = True, which is set to False by default. The frequency of saving models can be adjusted using save_model_interval configuration. Models will be saved in the result directory, under the folder called models. The directory corresponding each run will contain models saved throughout the experiment, each within a folder corresponding to the number of timesteps passed since starting the learning process.

Learnt models can be loaded using the checkpoint_path parameter, after which the learning will proceed from the corresponding timestep.

If you use this repository in your work, please cite the accompanying paper using the BibTeX citation below:

@article{naderializadeh2020graph,
  title={Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning},
  author={Naderializadeh, Navid and Hung, Fan H and Soleyman, Sean and Khosla, Deepak},
  journal={arXiv preprint arXiv:2010.04740},
  year={2020}
}