ramo 0.1.3

Algorithms for computing or learning equilibria in multi-objective games

Ramo

Ramo is an algorithmic game theory framework offering a collection of algorithms and utilities for computing or learning (approximate) equilibria in multi-objective games.

Please visit Ramo's docs for a complete overview of the framework and its capabilities.

Installation

Installing Ramo is as easy as typing

pip install ramo

If you would like to install from source, simply download this project as a zip file and place the folder at your desired location.

Contributing

We are building a library containing cutting edge research in multi-objective game theory. If you are working in this area and want to get involved, contributions are very welcome! Our focus is specifically on multi-objective games, but single-objective contributions are also welcome. If you are considering contributing, please send me a message ([e-mail]) so we can discuss how to move forward.

Citation

To cite the usage of this repository please use the following:

@misc{ropke2022ramo,
  author = {Willem Röpke},
  title = {Ramo: Rational Agents with Multiple Objectives},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/wilrop/mo-game-theory}},
}

This repository contains (derivatives of) original work that has been published elsewhere. We present a complete overview of which algorithms come from which work in references.md.

License

This project is licensed under the terms of the MIT license.