Corpus ID: 201666696

OpenSpiel: A Framework for Reinforcement Learning in Games

@article{Lanctot2019OpenSpielAF,
  title={OpenSpiel: A Framework for Reinforcement Learning in Games},
  author={Marc Lanctot and Edward Lockhart and Jean-Baptiste Lespiau and V. Zambaldi and Satyaki Upadhyay and Julien P{\'e}rolat and Sriram Srinivasan and Finbarr Timbers and K. Tuyls and Shayegan Omidshafiei and D. Hennes and Dustin Morrill and P. Muller and Timo Ewalds and R. Faulkner and J{\'a}nos Kram{\'a}r and Bart De Vylder and Brennan Saeta and J. Bradbury and David Ding and Sebastian Borgeaud and Matthew Lai and Julian Schrittwieser and T. Anthony and Edward Hughes and Ivo Danihelka and Jonah Ryan-Davis},
  journal={ArXiv},
  year={2019},
  volume={abs/1908.09453}
}
  • Marc Lanctot, Edward Lockhart, +24 authors Jonah Ryan-Davis
  • Published 2019
  • Computer Science, Mathematics
  • ArXiv
  • 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… CONTINUE READING
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