Metadata-Version: 2.1
Name: open_spiel
Version: 0.2.0rc3
Summary: A Framework for Reinforcement Learning in Games
Home-page: https://github.com/deepmind/open_spiel
Author: The OpenSpiel authors
Author-email: open_spiel@google.com
License: Apache 2.0
Description: 
        # 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 Status](https://travis-ci.org/deepmind/open_spiel.svg?branch=master)](https://travis-ci.org/deepmind/open_spiel)
        
        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. There is also a
        branch of pure Swift in the `swift` subdirectory.
        
        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).
        
        <p align="center">
          <img src="docs/_static/OpenSpielB.png" alt="OpenSpiel visual asset">
        </p>
        
        # 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)
        *   [Overview of Implemented Games](docs/games.md)
        *   [Overview of Implemented Algorithms](docs/algorithms.md)
        *   [Developer Guide](docs/developer_guide.md)
        *   [Guidelines and Contributing](docs/contributing.md)
        *   [Swift OpenSpiel](docs/swift.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, see the tutorial
        presentation slides:
        [Introduction to OpenSpiel](http://mlanctot.info/open_spiel-tutorial-kuleuven-mar11-2020.pdf).
        
        If you use OpenSpiel in your research, please cite the paper using the following
        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/)
        
        
Platform: UNKNOWN
Requires-Python: >=3
Description-Content-Type: text/markdown
