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OpenSpiel: common game interfaces and learning algorithms

Contribution grade

Multiagent game-learning systems · Human-designed research method or resource

Reference score135,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Provided a C++/Python framework spanning sequential/simultaneous and perfect/imperfect-information games, shared interfaces and algorithm implementations for reinforcement-learning and game-theoretic research.

Scientific scope

Provided a C++/Python framework spanning sequential/simultaneous and perfect/imperfect-information games, shared interfaces and algorithm implementations for reinforcement-learning and game-theoretic research. The record concerns the framework introduced in 2019. Features and supported-game counts from later living documentation are not attributed to that initial release. Integrating solvers and environments does not invent their underlying algorithms or games.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. The record concerns the framework introduced in 2019. Features and supported-game counts from later living documentation are not attributed to that initial release. Integrating solvers and environments does not invent their underlying algorithms or games.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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