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. 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.

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Please choose among the following options:

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.

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},
}

sudao-b/open_spiel

OpenSpiel: A Framework for Reinforcement Learning in Games

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