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Mean Field Multi-Agent Reinforcement Learning
| Date | Stars |
|---|---|
| 2026-07-31 | 422 |
| 2026-08-06 | 422 |
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# Mean Field Multi-Agent Reinforcement Learning
Implementation of MF-Q and MF-AC in the paper [Mean Field Multi-Agent Reinforcement Learning ](https://arxiv.org/pdf/1802.05438.pdf).
## Example
An 20x20 Ising model example under the low temperature.
<img src="https://github.com/mlii/mfrl/blob/master/resources/battle.gif" width='300' height='300'/>
A 40x40 Battle Game gridworld example with 128 agents, the blue one is MFQ, and the red one is IL.
## Code structure
- `main_MFQ_Ising.py`: contains code for running tabular based MFQ for Ising model.
- `./examples/`: contains scenarios for Ising Model and Battle Game (also models).
- `battle.py`: contains code for running Battle Game with trained model
- `train_battle.py`: contains code for training Battle Game models
## Compile Ising environment and run
**Requirements**
- `python==3.6.1`
- `gym==0.9.2` (might work with later versions)
- `matplotlib` if you would like to produce Ising model figures
## Compile MAgent platform and run
Before running Battle Game environment, you need to compile it. You can get more helps from: [MAgent](https://github.com/geek-ai/MAgent)
**Steps for compiling**
```shell
cd examples/battle_model
./build.sh
```
**Steps for training models under Battle Game settings**
1. Add python path in your `~/.bashrc` or `~/.zshrc`:
```shell
vim ~/.zshrc
export PYTHONPATH=./examples/battle_model/python:${PYTHONPATH}
source ~/.zshrc
```
2. Run training script for training (e.g. mfac):
```shell
python3 train_battle.py --algo mfac
```
or get help:
```shell
python3 train_battle.py --help
```
## Paper citation
If you found it helpful, consider citing the following paper:
<pre>
@InProceedings{pmlr-v80-yang18d,
title = {Mean Field Multi-Agent Reinforcement Learning},
author = {Yang, Yaodong and Luo, Rui and Li, Minne and Zhou, Ming and Zhang, Weinan and Wang, Jun},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
pages = {5567--5576},
year = {2018},
editor = {Dy, Jennifer and Krause, Andreas},
volume = {80},
series = {Proceedings of Machine Learning Research},
address = {Stockholmsmässan, Stockholm Sweden},
month = {10--15 Jul},
publisher = {PMLR}
}
</pre>
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matched fp:5bffa71d81fa5ad5, llm:Repository title and description: 'Mean Field Multi-Agent Reinforcement Learning'. Language: Python. No topics provided.
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