README

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 its 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 - Emulator of Atari 2600 ROMs simulated 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.

 Installation

To install the base Gymnasium library, use pip install gymnasium

This does not include dependencies for all families of environments (there's a massive number, and some can be problematic to install on certain systems). You can install these dependencies for one family like pip install "gymnasium[atari]" or use pip install "gymnasium[all]" to install all dependencies.

 API

The Gymnasium API models environments as simple Python env classes. Creating environment instances and interacting with them is very simple- here's an example using the "CartPole-v1" environment:

python
import gymnasium as gym
env = gym.make("CartPole-v1")

observation, info = env.reset(seed=42)
for _ in range(1000):
    action = env.action_space.sample()
    observation, reward, terminated, truncated, info = env.step(action)

    if terminated or truncated:
        observation, info = env.reset()
env.close()


 Notable Related Libraries

Please note that this is an incomplete list, and just includes libraries that the maintainers most commonly point newcomers to when asked for recommendations.

 CleanRL is a learning library based on the Gymnasium API. It is designed to cater to newer people in the field and provides very good reference implementations.
 PettingZoo is a multi-agent version o