open_spiel

# OpenSpiel: A Framework for Reinforcement Learning in Games 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) (for Linux and MacOS; see separate [Windows Installation](docs/windows.md) instructions.) * [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: ``` … ``` ## Versioning We use [Semantic Versioning](https://semver.org/).