This repository provides a reference implementation of the network architecture described in the ICLR 2024 paper NfgTransformer: Equivariant Representation Learning for Normal-form Games.
No installation is needed when interacting with the run_experiment.ipynb
notebook as it installs this package from GitHub sources directly.
For local installation, following the steps below:
Clone the repository:
git clone https://github.com/google-deepmind/nfg_transformer.git
Switch to the project directory:
cd nfg_transformer
Install dependencies:
pip install -e .
You can then run the tests to verify that all modules are working as intended
(requires pytest to be installed):
python -m pytest nfg_transformer/*test.py
The NfgTransformer offers general-purpose equivariant representation learning of normal-form games and can be used for equilibrium solving, max-deviation gain estimation and payoff prediction of n-player general-sum normal-form games.
run_experiment.ipynb implements a self-contained supervised learning
experiment for all these tasks and we recommend following along the notebook.
To cite this work:
@inproceedings{
liu2024nfgtransformer,
title={NfgTransformer: Equivariant Representation Learning for Normal-form Games},
author={Siqi Liu and Luke Marris and Georgios Piliouras and Ian Gemp and Nicolas Heess},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=4YESQqIys7}
}Copyright 2024 DeepMind Technologies Limited
All software is licensed under the Apache License, Version 2.0 (Apache 2.0); you may not use this file except in compliance with the Apache 2.0 license. You may obtain a copy of the Apache 2.0 license at: https://www.apache.org/licenses/LICENSE-2.0
All other materials are licensed under the Creative Commons Attribution 4.0 International License (CC-BY). You may obtain a copy of the CC-BY license at: https://creativecommons.org/licenses/by/4.0/legalcode
Unless required by applicable law or agreed to in writing, all software and materials distributed here under the Apache 2.0 or CC-BY licenses are distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the licenses for the specific language governing permissions and limitations under those licenses.
This is not an official Google product.