Polygames is a new open source AI research framework for training agents to master strategy games through self-play, rather than by studying extensive examples of successful gameplay. Because it is more flexible and has more features than previous frameworks, Polygames can help researchers with advancing and benchmarking a broad range of zero learning (ZL) techniques that don’t require training data sets.
Polygames’ architecture makes it compatible with more kinds of games — including Breakthrough, Hex, Havannah, Minishogi, Connect6, Minesweeper, Mastermind, EinStein würfelt nicht!, Nogo, and Othello — than previous systems, such as AlphaZero and ELF OpenGo. In addition to building and evaluating ZL methods across a variety of games, the tool allows researchers to study transfer learning, meaning the applicability of a model trained on one game to succeed at others. It provides a library of included games, as well as a single-file API to implement your own game.
The Facebook AI team demonstrated the effectiveness of Polygames as a training tool with strong model performances in various game competitions, including producing the first bot to beat a top-tier human player in the game 19×19 Hex. In addition to sharing the approach to building Polygames, Faceook is open-sourcing the full framework, which is available on GitHub.