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).

Installation

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 the experiments

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.

Kill running and dead process

bash clean.sh

Acknowledgement

This code base is implemented based on pymarl2(https://github.com/hijkzzz/pymarl2)