This is an official pytorch implementation of the paper Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rollouts.
Note: We re-factor the code and the results are slightly different with those in the paper.
git clone [email protected]:Leo-xh/AORPO.git
cd aorpo
conda create -n aorpo python=3.7
conda activate aorpo
pip install -r requirements.txt
We provide a shell script and the command is
./train.sh [tag] [env_name] [alg] {gpu id}
We recomment training with gpus since AORPO is hard to train without gpus.
For example, to train AORPO in Cooperative Navigation
./train.sh test spread AORPO 0
Please feel free to try other parameters.
Since training AORPO requires plenty of time, we provide trained models (without large dynammics models) in ./trained_models/
, the models can be evaluated using the following command
python eval.py [env_id] [model_path] {--render}
For example, to evaluate AORPO in Cooperative Navigation
python eval.py simple_spread ./trained_models/simple_spread.pt --render
@article{2021,
title={Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rollouts},
url={https://dx.doi.org/10.24963/ijcai.2021/466},
DOI={10.24963/ijcai.2021/466},
journal={Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence},
publisher={International Joint Conferences on Artificial Intelligence Organization},
author={Zhang, Weinan and Wang, Xihuai and Shen, Jian and Zhou, Ming},
year={2021},
month={Aug} }