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Code for "Actor-Attention-Critic for Multi-Agent Reinforcement Learning" ICML 2019
| Date | Stars |
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| 2026-07-31 | 807 |
| 2026-08-01 | 808 |
| 2026-08-05 | 808 |
| 2026-08-06 | 808 |
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# Multi-Actor-Attention-Critic
Code for [*Actor-Attention-Critic for Multi-Agent Reinforcement Learning*](https://arxiv.org/abs/1810.02912) (Iqbal and Sha, ICML 2019)
## Requirements
* Python 3.6.1 (Minimum)
* [OpenAI baselines](https://github.com/openai/baselines), commit hash: 98257ef8c9bd23a24a330731ae54ed086d9ce4a7
* My [fork](https://github.com/shariqiqbal2810/multiagent-particle-envs) of Multi-agent Particle Environments
* [PyTorch](http://pytorch.org/), version: 0.3.0.post4
* [OpenAI Gym](https://github.com/openai/gym), version: 0.9.4
* [Tensorboard](https://github.com/tensorflow/tensorboard), version: 0.4.0rc3 and [Tensorboard-Pytorch](https://github.com/lanpa/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:
```shell
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:
```bibtex
@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},
}
```
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3d2f175d7a5e4894, desc:multi-agent, desc:multi agent