Awesome
Multi-Actor-Attention-Critic
Code for Actor-Attention-Critic for Multi-Agent Reinforcement Learning (Iqbal and Sha, ICML 2019)
Requirements
- Python 3.6.1 (Minimum)
- OpenAI baselines, commit hash: 98257ef8c9bd23a24a330731ae54ed086d9ce4a7
- My fork of Multi-agent Particle Environments
- PyTorch, version: 0.3.0.post4
- OpenAI Gym, version: 0.9.4
- Tensorboard, version: 0.4.0rc3 and Tensorboard-Pytorch, version: 1.0 (for logging)
The versions are just what I used and not necessarily strict requirements.
How to Run
All training code is contained within main.py
. To view options simply run:
python main.py --help
The "Cooperative Treasure Collection" environment from our paper is referred to as fullobs_collect_treasure
in this repo, and "Rover-Tower" is referred to as multi_speaker_listener
.
In order to match our experiments, the maximum episode length should be set to 100 for Cooperative Treasure Collection and 25 for Rover-Tower.
Citing our work
If you use this repo in your work, please consider citing the corresponding paper:
@InProceedings{pmlr-v97-iqbal19a,
title = {Actor-Attention-Critic for Multi-Agent Reinforcement Learning},
author = {Iqbal, Shariq and Sha, Fei},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {2961--2970},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
address = {Long Beach, California, USA},
month = {09--15 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v97/iqbal19a/iqbal19a.pdf},
url = {http://proceedings.mlr.press/v97/iqbal19a.html},
}