/ Machine Learning

A Framework for Reinforcement Learning in Games

A Framework for Reinforcement Learning in Games

OpenSpiel

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.

GitHub