Represented Value Function Approach for Large Scale Multi Agent Reinforcement Learning

A Tensorflow implementation of RFAC and RFQ in the paper Represented Value Function Approach for Large Scale Multi Agent Reinforcement Learning .

This work is based on the code framework in MFQ-MFAC

(train 64 agents and test 500 agents in wild war senario)

• ./examples/: contains scenarios for Game and models.
• battle.py: contains code for running Battle Game with trained model
• wild_war.py: contains code for running Wild war Game with trained model
• train_battle.py: contains code for training Battle Game models
• wildwar_ELO_single.py: contains code for testing Wild war game by Elo Scores among all players
• battle_ELO_single: contains code for testing Battle game by Elo Scores among all players
• data: pre-trained model

Requirements

• python>=3.6.0
• tensorflow>=1.14.0

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

Steps for compiling

1. cd Represented_Value Function_MARL/examples/battle_model

2. bash build.sh

Steps for training models under Battle Game settings

1. cd Represented_Value Function_MARL

   export PYTHONPATH=./examples/battle_model/python:${PYTHONPATH}

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

   python train_battle.py --algo rfac

3. train your model and change the name of model file form 1999 to 1999A,1999B,...

Steps for testing models under Battle Game and Wild_war Game

1. Battle Game

   python battle.py --algo mfq --oppo rfac --idx {1999A,1999A}

2. Wild war Game

   python wild_war.py --algo mfq --oppo rfac --idx {1999A,1999A}

3. Compute Elo scores

   python battle_ELO_single.py

   python wildwar_ELO_single.py

4. once you open a terminal,type:

   export PYTHONPATH=./examples/battle_model/python:${PYTHONPATH}

   or you can edit the ~/.bashrc file to save time.

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

@InProceedings{
  title = 	 {Represented Value Function Approach for Large Scale Multi Agent Reinforcement Learning},
  author = 	 {Weiya Ren},
  booktitle = 	 {arXiv:2001.01096v1},
  year = 	 {2020},
  address = 	 {China},
  month = 	 {4 Jan}
}

@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}
}