Implementation of MF-Q and MF-AC in the paper Mean Field Multi-Agent Reinforcement Learning .

An 20x20 Ising model example under the low temperature.

A 40x40 Battle Game gridworld example with 128 agents, the blue one is MFQ, and the red one is IL.

• 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

Requirements

• python==3.6.1
• gym==0.9.2 (might work with later versions)
• matplotlib if you would like to produce Ising model figures

Before running Battle Game environment, you need to compile it. You can get more helps from: MAgent

Steps for compiling

cd examples/battle_model
./build.sh

Steps for training models under Battle Game settings

1. Add python path in your ~/.bashrc or ~/.zshrc:

   vim ~/.zshrc
   export PYTHONPATH=./examples/battle_model/python:${PYTHONPATH}
   source ~/.zshrc

2. Run training script for training (e.g. mfac):

   python3 train_battle.py --algo mfac

   or get help:

   python3 train_battle.py --help

If you found it helpful, consider citing the following paper:

@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}
}