The RL toolkit that standardized environments, then bowed out

What it does

Gym provides a standard Python API that lets reinforcement learning algorithms talk to environments through a simple env class interface: make, reset, step, and close. It ships with a built-in collection of environments including CartPole and Atari, and its API became the de facto field standard for RL research. The project is now frozen; since 2021, maintenance has moved to Gymnasium under the Farama Foundation, and OpenAI Gym itself will receive no further updates.

The interesting bit

The README treats its own obsolescence as the lead story, which is refreshingly honest. Rather than lingering in limbo, the maintainers formally passed the torch to Gymnasium, a drop-in replacement, and locked the original repository in place.

Key highlights

• Simple, rigid env API that became the field standard for RL environments
• Strict environment versioning (e.g., CartPole-v1) to keep research results reproducible
• Large surrounding ecosystem including CleanRL, Tianshou, RLlib, and PettingZoo
• MuJoCo environments updated to v4, shedding the old mujoco-py dependency
• All future development moved to Gymnasium; this repo is effectively legacy

Caveats

• No future updates or bug fixes planned; new projects should use Gymnasium
• Windows is not officially supported, though community PRs are accepted
• Full environment dependencies can be problematic to install on some systems

Verdict

Care if you are maintaining legacy RL code that depends on the original gym import. Ignore if you are starting fresh; head straight to Gymnasium instead.