Weighted QMIX: Expanding Monotonic Value Function Factorisation (NeurIPS 2020)
Based on PyMARL (https://github.com/oxwhirl/pymarl/). Please refer to that repo for more documentation.
This repo contains the cleaned-up code that was used in "Weighted QMIX: Expanding Monotonic Value Function Factorisation" (https://arxiv.org/abs/2006.10800).
Included in this repo
In particular implementations for:
- OW-QMIX
- CW-QMIX
- Versions of DDPG & SAC used in the paper
We thank the authors of "QPLEX: Duplex Dueling Multi-Agent Q-Learning" (https://arxiv.org/abs/2008.01062) for their implementation of QPLEX (https://github.com/wjh720/QPLEX/), whose implementation we used. The exact implementation we used is included in this repo.
Note that in the repository the naming of certain hyper-parameters and concepts is a little different to the paper:
- α in the paper is
w
in the code - Optimistic Weighting (OW) is referred to as
hysteretic_qmix
For all SMAC experiments we used SC2.4.6.2.69232 (not SC2.4.10). The underlying dynamics are sufficiently different that you cannot compare runs across the 2 versions!
The install_sc2.sh
script will install SC2.4.6.2.69232.
Running experiments
The config files (src/config/algs/*.yaml
) contain default hyper-parameters for the respective algorithms. These were changed when running the experiments for the paper (epsilon_anneal_time = 1000000
for the robustness to exploration experiments, and w=0.1
for the predator prey punishment experiments for instance). Please see the Appendix of the paper for the exact hyper-parameters used.
Set central_mixer=atten
to get the modified mixing network architecture that was used for the final experiment on corridor
in the paper.
As an example, to run the OW-QMIX on 3s5z with epsilon annealed over 1mil timesteps using docker:
bash run.sh $GPU python3 src/main.py --config=ow_qmix --env-config=sc2 with env_args.map_name=3s5z w=0.5 epsilon_anneal_time=1000000
Citing
Bibtex:
@inproceedings{rashid2020weighted,
title={Weighted QMIX: Expanding Monotonic Value Function Factorisation},
author={Rashid, Tabish and Farquhar, Gregory and Peng, Bei and Whiteson, Shimon},
booktitle={Advances in Neural Information Processing Systems},
year={2020}
}