https://github.com/deepmind/open_spiel

OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
https://github.com/deepmind/open_spiel

cpp games multiagent python reinforcement-learning

Last synced: about 2 months ago
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OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.

• Host: GitHub
• URL: https://github.com/deepmind/open_spiel
• Owner: google-deepmind
• License: apache-2.0
• Created: 2019-07-22T13:50:21.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2024-04-21T11:38:33.000Z (5 months ago)
• Last Synced: 2024-04-22T12:00:32.877Z (5 months ago)
• Topics: cpp, games, multiagent, python, reinforcement-learning
• Language: C++
• Homepage:
• Size: 27.1 MB
• Stars: 3,996
• Watchers: 108
• Forks: 886
• Open Issues: 35
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-google-colab - OpenSpiel - Collection of environments and algorithms for research in general reinforcement learning and search/planning in games. (Technologies)
• awesome-deep-rl - DeepMind OpenSpiel - A collection of environments and algorithms for research in general reinforcement learning and search/planning in games. (Libraries)

README

        

# OpenSpiel: A Framework for Reinforcement Learning in Games

[![Documentation Status](https://readthedocs.org/projects/openspiel/badge/?version=latest)](https://openspiel.readthedocs.io/en/latest/?badge=latest)

![build_and_test](https://github.com/deepmind/open_spiel/workflows/build_and_test/badge.svg)

OpenSpiel is a collection of environments and algorithms for research in general

reinforcement learning and search/planning in games. OpenSpiel supports n-player

(single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and

sequential, strictly turn-taking and simultaneous-move, perfect and imperfect

information games, as well as traditional multiagent environments such as

(partially- and fully- observable) grid worlds and social dilemmas. OpenSpiel

also includes tools to analyze learning dynamics and other common evaluation

metrics. Games are represented as procedural extensive-form games, with some

natural extensions. The core API and games are implemented in C++ and exposed to

Python. Algorithms and tools are written both in C++ and Python.

To try OpenSpiel in Google Colaboratory, please refer to `open_spiel/colabs` subdirectory or start [here](https://colab.research.google.com/github/deepmind/open_spiel/blob/master/open_spiel/colabs/install_open_spiel.ipynb).



  



# Index

Please choose among the following options:

*   [Installing OpenSpiel](docs/install.md)

*   [Introduction to OpenSpiel](docs/intro.md)

*   [API Overview and First Example](docs/concepts.md)

*   [API Reference](docs/api_reference.md)

*   [Overview of Implemented Games](docs/games.md)

*   [Overview of Implemented Algorithms](docs/algorithms.md)

*   [Developer Guide](docs/developer_guide.md)

*   [Using OpenSpiel as a C++ Library](docs/library.md)

*   [Guidelines and Contributing](docs/contributing.md)

*   [Authors](docs/authors.md)

For a longer introduction to the core concepts, formalisms, and terminology,

including an overview of the algorithms and some results, please see

[OpenSpiel: A Framework for Reinforcement Learning in Games](https://arxiv.org/abs/1908.09453).

For an overview of OpenSpiel and example uses of the core API, please check out

our tutorials:

*   [Motivation, Core API, Brief Intro to Replictor Dynamics and Imperfect

    Information Games](https://www.youtube.com/watch?v=8NCPqtPwlFQ) by Marc

    Lanctot.

    [(slides)](http://mlanctot.info/files/OpenSpiel_Tutorial_KU_Leuven_2022.pdf)

    [(colab)](https://colab.research.google.com/github/deepmind/open_spiel/blob/master/open_spiel/colabs/OpenSpielTutorial.ipynb)

*   [Motivation, Core API, Implementing CFR and REINFORCE on Kuhn poker, Leduc

    poker, and Goofspiel](https://www.youtube.com/watch?v=o6JNHoGUXCo) by Edward

    Lockhart.

    [(slides)](http://mlanctot.info/files/open_spiel_tutorial-mar2021-comarl.pdf)

    [(colab)](https://colab.research.google.com/github/deepmind/open_spiel/blob/master/open_spiel/colabs/CFR_and_REINFORCE.ipynb)

If you use OpenSpiel in your research, please cite the paper using the following

BibTeX:

```bibtex

@article{LanctotEtAl2019OpenSpiel,

  title     = {{OpenSpiel}: A Framework for Reinforcement Learning in Games},

  author    = {Marc Lanctot and Edward Lockhart and Jean-Baptiste Lespiau and

               Vinicius Zambaldi and Satyaki Upadhyay and Julien P\'{e}rolat and

               Sriram Srinivasan and Finbarr Timbers and Karl Tuyls and

               Shayegan Omidshafiei and Daniel Hennes and Dustin Morrill and

               Paul Muller and Timo Ewalds and Ryan Faulkner and J\'{a}nos Kram\'{a}r

               and Bart De Vylder and Brennan Saeta and James Bradbury and David Ding

               and Sebastian Borgeaud and Matthew Lai and Julian Schrittwieser and

               Thomas Anthony and Edward Hughes and Ivo Danihelka and Jonah Ryan-Davis},

  year      = {2019},

  eprint    = {1908.09453},

  archivePrefix = {arXiv},

  primaryClass = {cs.LG},

  journal   = {CoRR},

  volume    = {abs/1908.09453},

  url       = {http://arxiv.org/abs/1908.09453},

}

```

## Versioning

We use [Semantic Versioning](https://semver.org/).