A standard API for reinforcement learning and a diverse set of reference environments (formerly Gym)
Project description
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
ingymnasium.make
if necessary.
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