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.
Please choose among the following options:
- Installing OpenSpiel
- Introduction to OpenSpiel
- API Overview and First Example
- Overview of Implemented Games
- Developer Guide
- Guidelines and Contributing
- Swift OpenSpiel
- Authors
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.