gymnasium 0.26.3

A standard API for reinforcement learning and a diverse set of reference environments (formerly Gym)

Gymnasium is an open source Python library for developing and comparing reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API. This is a fork of OpenAI's Gym library by the maintainers (OpenAI handed over maintenance a few years ago to an outside team), and is where future maintenance will occur going forward

The documentation website is at gymnasium.farama.org, and we have a public discord server (which we also use to coordinate development work) that you can join here: https://discord.gg/bnJ6kubTg6

Environments

Gymnasium includes the following families of environments along with a wide variety of third-party environments

• Classic Control - These are classic reinforcement learning based on real-world problems and physics.
• Box2D - These environments all involve toy games based around physics control, using box2d based physics and PyGame-based rendering
• Toy Text - These environments are designed to be extremely simple, with small discrete state and action spaces, and hence easy to learn. As a result, they are suitable for debugging implementations of reinforcement learning algorithms.
• MuJoCo - A physics engine based environments with multi-joint control which are more complex than the Box2D environments.
• Atari - A set of 57 Atari 2600 environments simulated through Stella and the Arcade Learning Environment that have a high range of complexity for agents to learn.
• Third-party - A number of environments have been created that are compatible with the Gymnasium API. Be aware of the version that the software was created for and use the apply_env_compatibility in gymnasium.make if necessary.