Gymnasium vs reinforcement-learning-an-introduction
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A standard Python interface for reinforcement learning environments, plus reference environments to test algor
Open source · MIT
Python code that reproduces the figures and examples from the Sutton & Barto reinforcement learning textbook.
The short answer
Choose Gymnasium if you need…
- Researchers comparing reinforcement learning algorithms on the same environments
- Students learning reinforcement learning with simple, well-known test problems
- Developers who want an environment API that other tools (such as CleanRL) already support
- Anyone migrating older Gym code who wants continued maintenance
Choose reinforcement-learning-an-introduction if you need…
- Students working through the Sutton & Barto book who want runnable versions of its examples
- Readers who learn better by seeing plotted results of algorithms like Sarsa, Q-learning, or Dyna-Q
- Developers who want simple Python reference implementations of classic RL methods
Side-by-side
| Gymnasium | reinforcement-learning-an-introduction | |
|---|---|---|
| Type | Library | Learning resource |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 12.8k | 14.8k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Clone and build from source |
| Main language | Python | Python |
| Last code update | 7 days ago | 2 years ago |
What each one is
Gymnasium
Gymnasium is an open-source Python library that gives reinforcement learning algorithms and simulated environments a shared, standard way to talk to each other. It also includes a set of ready-made environments, such as classic control problems, physics-based games, and Atari games. It is a maintained fork of OpenAI's Gym library.
reinforcement-learning-an-introduction
This repository contains Python code that replicates the experiments and figures from the book Reinforcement Learning: An Introduction (2nd Edition) by Sutton and Barto. It is organized by chapter, covering topics such as bandit problems, dynamic programming, Monte Carlo methods, temporal-difference learning, and function approximation. People use it to see how the book's algorithms behave when run on the textbook's examples.
Best for
Gymnasium
- Researchers comparing reinforcement learning algorithms on the same environments
- Students learning reinforcement learning with simple, well-known test problems
- Developers who want an environment API that other tools (such as CleanRL) already support
- Anyone migrating older Gym code who wants continued maintenance
reinforcement-learning-an-introduction
- Students working through the Sutton & Barto book who want runnable versions of its examples
- Readers who learn better by seeing plotted results of algorithms like Sarsa, Q-learning, or Dyna-Q
- Developers who want simple Python reference implementations of classic RL methods
Strengths
Gymnasium
- Provides a standard API, so the same agent code can run on many environments
- Maintained by a team that took over from OpenAI's Gym, with documentation and a Discord community
- Environment versions are pinned with suffixes like -v1, which helps reproducibility
reinforcement-learning-an-introduction
- Organized chapter by chapter so it maps directly onto the book
- Covers a broad range of chapters, from tabular methods through function approximation
- Released under the MIT license, so it is free to read and reuse
Not ideal for
Gymnasium
- People who want a finished product rather than code to build on, since it is a library for experiments and research
- Users who need multi-agent environments, because the README says Gymnasium covers single-agent setups and points to PettingZoo for multi-agent work
- Those who do not know Python, since the API and examples are Python-only
reinforcement-learning-an-introduction
- People looking for a general-purpose RL library for production use, since the README presents this as a book replication
- Anyone expecting exercise answers, because the author states they do not have exercise answers for the book
Trade-offs
Gymnasium
- Installing the base package does not include dependencies for every environment family; some extras can be difficult to install on certain systems
- Third-party environments may target older API versions and may need the apply_env_compatibility option
- The README is short on detail for advanced use, so deeper usage depends on the separate documentation site
reinforcement-learning-an-introduction
- The README is short and the contents list is truncated, so coverage of each chapter is not fully documented here
- The author does not provide exercise solutions, so readers must work those out themselves
- Last push was 2024-08-09 and there is no release, so maintenance status is unclear
Getting started
Gymnasium
Install the base library
pip install gymnasiumInstall dependencies for one environment family (example: Atari)
pip install "gymnasium[atari]"reinforcement-learning-an-introduction
Clone the repository
git clone https://github.com/ShangtongZhang/reinforcement-learning-an-introduction.gitFrequently asked questions
What is the difference between Gymnasium and reinforcement-learning-an-introduction?
Gymnasium: A standard Python interface for reinforcement learning environments, plus reference environments to test algor. reinforcement-learning-an-introduction: Python code that reproduces the figures and examples from the Sutton & Barto reinforcement learning textbook. Gymnasium suits researchers comparing reinforcement learning algorithms on the same environments, while reinforcement-learning-an-introduction suits students working through the sutton & barto book who want runnable versions of its examples.
Are Gymnasium and reinforcement-learning-an-introduction free?
Gymnasium: Free and open source. reinforcement-learning-an-introduction: Free and open source.
Is Gymnasium or reinforcement-learning-an-introduction more popular?
reinforcement-learning-an-introduction has more GitHub stars (14.8k) than Gymnasium (12.8k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Gymnasium and reinforcement-learning-an-introduction?
Other options people compare them with include Stable Baselines3, Spinning Up.
Keep comparing
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