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Gymnasium Documentation. An API standard for reinforcement learning with a diverse collection of reference environments. Gymnasium is a maintained fork of OpenAI’s Gym library. The Gymnasium interface is simple, pythonic, and capable of representing general RL problems, and has a compatibility wrapper for old Gym environments:
- Basic Usage
Basic Usage # Gymnasium is a project that provides an API...
- Compatibility with Gym
Gym v0.21 Environment Compatibility#. A number of...
- v21 to v26 Migration Guide
Gymnasium v26 changed to using numpy.random.Generator...
- Env
The main Gymnasium class for implementing Reinforcement...
- Register and Make
Parameters:. print_registry – Environment registry to be...
- Spaces
This module implements various spaces. Spaces describe...
- Wrappers
Wraps a gymnasium.Env to allow a modular transformation of...
- Vector
Note. The info parameter of reset() and step() was...
- Basic Usage
The Gym interface is simple, pythonic, and capable of representing general RL problems: All development of Gym has been moved to Gymnasium, a new package in the Farama Foundation that's maintained by the same team of developers who have maintained Gym for the past 18 months. If you're already using the latest release of Gym (v0.26.2), then you ...
- Overview
- Environments
- Installation
- API
- Notable Related Libraries
- Environment Versioning
- Development Roadmap
- Support Gymnasium's Development
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
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.
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.
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:
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 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 of Gymnasium with a number of implemented environments, i.e. multi-agent Atari environments.
•The Farama Foundation also has a collection of many other environments that are maintained by the same team as Gymnasium and use the Gymnasium API.
Gymnasium 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. These inherent from Gym.
We have a roadmap for future development work for Gymnasium available here: #12
If you are financially able to do so and would like to support the development of Gymnasium, please join others in the community in donating to us.
/ docs. README.md. Cannot retrieve latest commit at this time. History. 61 lines (36 loc) · 2.8 KB. Gymnasium-docs. This folder contains the documentation for Gymnasium. If you are modifying an atari environment page, please follow the instructions below. For more information about how to contribute to the documentation go to our CONTRIBUTING.md.
21 de ago. de 2023 · 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.
