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OpenSpiel: A Framework for Reinforcement Learning in Games

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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.

OpenSpiel visual asset

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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.

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