Attention-Guided Contrastive Role Representations for Multi-agent Reinforcement Learning

Zican Hu, Zongzhang Zhang, Huaxiong Li, Chunlin Chen, Hongyu Ding, Zhi Wang*

A link to our paper can be found on Paper Link

Overview

Instructions

ACORM tested on two benchmark tasks SMAC and GRF based on two algorithm framework QMIX and MAPPO.

Citation

Please cite our paper as:

@inproceedings{
hu2024attentionguided,
title={Attention-Guided Contrastive Role Representations for Multi-agent Reinforcement Learning},
author={Zican Hu and Zongzhang Zhang and Huaxiong Li and Chunlin Chen and Hongyu Ding and Zhi Wang},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=LWmuPfEYhH}
}

experiment instructions

Installation instructions

Download the Linux version 4.10 of StarCraft II from the Blizzard's repository. By default, the game is expected to be in ~/StarCraftII/ directory. See requirments.txt file for more information about how to install the dependencies.

conda create -n acorm python=3.9.16 -y
conda activate acorm
pip install -r requirements.txt

Run an experiment

You can execute the following command to run ACORM based on QMIX with a map config, such as MMM2:

python ./ACORM_QMIX/main.py --algorithm ACORM --env_name MMM2 --cluster_num 3 --max_train_steps 3050000

or you can execute the following command to run ACORM base on MAPPO with a map config, such as corridor

python ./ACORM_MAPPO/main.py --algorithm ACORM --env_name corridor --cluster_num 3 --max_train_steps 5050000

All results will be stored in the ACORM_QMIX or ACORM_MAPPO/results folder. You can see the console output, config, and tensorboard logging in the ACORM_QMIX or ACORM_MAPPO/results/tb_logs folder.

You can plot the curve with seaborn:

python plot.py --algorithm 'ACORM_QMIX' or 'ACORM_MAPPO'

License

Code licensed under the Apache License v2.0.