OpenAI: Gym Retro

OpenAI: Gym Retro

We’re releasing the full version of Gym Retro, a platform for reinforcement learning research on games. This brings our publicly-released game count from around 70 Atari games and 30 Sega games to over 1,000 games across a variety of backing emulators. We’re also releasing the tool we use to add new games to the platform.

We use Gym Retro to conduct research on RL algorithms and study generalization. Prior research in RL has mostly focused on optimizing agents to solve single tasks. With Gym Retro, we can study the ability to generalize between games with similar concepts but different appearances.

This release includes games from the Sega Genesis and Sega Master System, and Nintendo’s NES, SNES, and Game Boy consoles. It also includes preliminary support for the Sega Game Gear, Nintendo Game Boy Color, Nintendo Game Boy Advance, and NEC TurboGrafx.

Some of the released game integrations, included those games in the data/experimental folder of Gym Retro, are in a beta state — please try them out and let us know if you encounter any bugs. Due to the large scale of the changes involved the code will only be available on a branch for the time being. To avoid breaking contestants’ code we won’t be merging the branch until after the contest concludes.