This code is build based on PyMARL2 and PyMARL. We assume that you have experience with PyMARL. The requirements are the same as PyMARL2.
cd ResQ
python3 src/main.py --config=ResQ --env-config=sc2 with env_args.map_name=2s3z
cd ResZ
python3 src/main.py --config=ResZ --env-config=sc2 with env_args.map_name=2s3z
The config files act as defaults for an algorithm or environment.
They are all located in src/config
.
--config
refers to the config files in src/config/algs
--env-config
refers to the config files in src/config/envs
All results will be stored in the Results
folder.
If you use ResQ/ResZ in your research, please cite our paper
Siqi Shen, Mengwei Qiu, Jun Liu, Weiquan Liu, Yongquan Fu, Xinwang Liu, Cheng Wang. ResQ: A Residual Q Function-based Approach for Multi-Agent Reinforcement Learning Value Factorization. NeurIPS 2022
In BibTeX format:
@inproceedings{ResQ,
author = {Siqi Shen and Mengwei Qiu and Jun Liu and Weiquan Liu and Yongquan Fu and Xinwang Liu and Cheng Wang},
title = {ResQ: A Residual Q Function-based Approach for Multi-Agent Reinforcement Learning Value Factorization},
booktitle = {{NeurIPS}},
year = {2022}}
Code licensed under the Apache License v2.0