PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement Learning (NeurIPS 2022)
This is the implementation of the paper "PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement Learning" (NeurIPS 2022).
Install dependencies :
conda create -n pymarl python=3.8 -y
conda activate pymarl
bash install_dependencies.sh
Install SC2 :
bash install_sc2.sh
This will download SC2.4.10 into the 3rdparty folder and copy the maps necessary to run over.
Run all experiments
bash FULL_run.sh
or run single experiment
python src/main.py --config=ow_qmix --env-config=sc2 with env_args.map_name=6h_vs_8z w=0.5 epsilon_anneal_time=500000 t_max=5005000
All results will be stored in the results
folder.
bash clean.sh
This code base is implemented based on pymarl2(https://github.com/hijkzzz/pymarl2)