Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition

Official codebase for paper Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition. This codebase is based on the open-source PyMARL framework and please refer to that repo for more documentation.

1. Prerequisites

Install dependencies

See requirment.txt file for more information about how to install the dependencies.

Install StarCraft II

Please use the Blizzard's repository to download the Linux version 4.10 of StarCraft II. By default, the game is expected to be in ~/StarCraftII/ directory. This can be changed by setting the environment variable SC2PATH.

- Please pay attention to the version of SC2 you are using for your experiments. 
- We use the latest version SC2.4.10 for all SMAC experiments instead of SC2.4.6.2.69232.
- Performance is not comparable across versions.

The SMAC maps used for all experiments is in CIA/src/envs/starcraft2/maps/SMAC_Maps directory. You should place the SMAC_Maps directory in StarCraftII/Maps.

2. Usage

Please follow the instructions below to replicate the results in the paper.

Didactic Games: Turn

# QMIX
python src/main.py --config=qmix_turn --env-config=turn with env_args.map_name=turn

# QMIX (CIA)
python src/main.py --config=cia_grad_qmix_turn --env-config=turn with env_args.map_name=turn 

SMAC

# QMIX
python src/main.py --config=qmix_[map_name] --env-config=sc2 with env_args.map_name=[map_name]

# QPLEX
python src/main.py --config=qplex_[map_name] --env-config=sc2 with env_args.map_name=[map_name]

# QMIX (CIA)
python src/main.py --config=cia_grad_qmix_[map_name] --env-config=sc2 with env_args.map_name=[map_name] 

# QPLEX (CIA)
python src/main.py --config=cia_qplex_[map_name] --env-config=sc2 with env_args.map_name=[map_name]

3. Citation

If you find this work useful for your research, please cite our paper:

@inproceedings{liu2023CIA,
  title={Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition},
  author={Liu, Shunyu and Zhou, Yihe and Song, Jie and Zheng, Tongya and Chen, Kaixuan and Zhu, Tongtian and Feng, Zunlei and Song, Mingli},
  booktitle={AAAI Conference on Artificial Intelligence},
  year={2023}
}