The team that has been maintaining Gym since 2021 has moved all future development to Gymnasium, a drop in replacement for Gym (import gymnasium as gym), and Gym will not be receiving any future updates. Please switch over to Gymnasium as soon as you're able to do so. If you'd like to read more about the story behind this switch, please check out this blog post.
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.dev/, and you can propose fixes and changes to it here.
Gym also has a discord server for development purposes that you can join here: https://discord.gg/nHg2JRN489
First, create a new virtual environment (optional but highly recommended) using your preferred tool.
conda create -n gym python=3.9
conda activate gym
To install the base Gym library, use
python -m pip install 'git+https://github.com/mikesongming/gym.git#egg=gym[all]'
We support Python 3.9, 3.10 on macOS.
Modify moviepy's code for use_clip_fps_by_default in decorators.py:L128:
# names = func_code.co_varnames[1:]
import inspect
names = inspect.getfullargspec(f).args[1:]
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)
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()Please note that this is an incomplete list, and just includes libraries that the maintainers most commonly point newcommers to when asked for recommendations.
- CleanRL is a learning library based on the Gym API. It is designed to cater to newer people in the field and provides very good reference implementations.
- Tianshou is a learning library that's geared towards very experienced users and is design to allow for ease in complex algorithm modifications.
- RLlib is a learning library that allows for distributed training and inferencing and supports an extraordinarily large number of features throughout the reinforcement learning space.
- 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.
The latest "_v4" and future versions of the MuJoCo environments will no longer depend on mujoco-py. Instead mujoco will be the required dependency for future gym MuJoCo environment versions. Old gym MuJoCo environment versions that depend on mujoco-py are REMOVED.
To install the dependencies for the latest gym MuJoCo environments use pip install gym[mujoco].
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.