README.md

QMIXRNN

Referring to pymarl, qmix is implemented clearly with RNN to cope with SMAC environment. This clear implementation can help you figure out how does QMIX work

Run

StarCraft2 version: SC2.4.6.2.69232 (harder than SC2.4.10)

CUDA_VISIBLE_DEVICES=0 python main.py --map-name=3s5z --seed=0

TODO

Now this code can do very good on part of easy scenarios like 1c3s5z, 2s3z, 3s5z, 8m, 2s_vs_1sc, 3m and 10m_vs_11m, which is similar with the experiment results in The StarCraft Multi-Agent Challenge. But it's not good on hard and superhard scenarios like 5m_vs_6m and 2c_vs_64zg and so on.

I'm trying to approach the result of pymarl. At the same time, I'm also trying to achieve some tricks on this code like multi step TD target and so on.

Reference

@inproceedings{rashid2018qmix,
  title={Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning},
  author={Rashid, Tabish and Samvelyan, Mikayel and Schroeder, Christian and Farquhar, Gregory and Foerster, Jakob and Whiteson, Shimon},
  booktitle={International conference on machine learning},
  pages={4295--4304},
  year={2018},
  organization={PMLR}
}

@article{samvelyan19smac,
  title = {{The} {StarCraft} {Multi}-{Agent} {Challenge}},
  author = {Mikayel Samvelyan and Tabish Rashid and Christian Schroeder de Witt and Gregory Farquhar and Nantas Nardelli and Tim G. J. Rudner and Chia-Man Hung and Philiph H. S. Torr and Jakob Foerster and Shimon Whiteson},
  journal = {CoRR},
  volume = {abs/1902.04043},
  year = {2019},
}