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Gym 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. Since its release, Gym's API has become the field standard for doing this.

Gym documentation website is at https://www.gymlibrary.ml/, and you can propose fixes and changes here.

To install the base Gym library, use pip install gym.

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 gym[atari] or use pip install gym[all] to install all dependencies.

We support Python 3.7, 3.8, 3.9 and 3.10 on Linux and macOS. We will accept PRs related to Windows, but do not officially support it.

The Gym API's 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:

import gym
env = gym.make("CartPole-v1")
observation, info = env.reset(seed=42, return_info=True)

for _ in range(1000):
    action = env.action_space.sample()
    observation, reward, done, info = env.step(action)

    if done:
        observation, info = env.reset(return_info=True)
env.close()

• Stable Baselines 3 is a learning library based on the Gym API. It is our recommendation for beginners who want to start learning things quickly.
• RL Baselines3 Zoo builds upon SB3, containing optimal hyperparameters for Gym environments as well as code to easily find new ones. Such tuning is almost always required.
• The Autonomous Learning Library and Tianshou are two reinforcement learning libraries I like that are generally geared towards more experienced users.
• RLlib is a commonly used library that allows for distributed training and inferencing.
• PettingZoo is like Gym, but for environments with multiple agents.

Gym keeps strict versioning for reproducibility reasons. All environments end in a suffix like "_v0". When changes are made to environments that might impact learning results, the number is increased by one to prevent potential confusion.

A whitepaper from when Gym just came out is available https://arxiv.org/pdf/1606.01540, and can be cited with the following bibtex entry:

@misc{1606.01540,
  Author = {Greg Brockman and Vicki Cheung and Ludwig Pettersson and Jonas Schneider and John Schulman and Jie Tang and Wojciech Zaremba},
  Title = {OpenAI Gym},
  Year = {2016},
  Eprint = {arXiv:1606.01540},
}

There used to be release notes for all the new Gym versions here. New release notes are being moved to releases page on GitHub, like most other libraries do. Old notes can be viewed here.