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.

Gymnasium

Open source · MIT

A standard Python interface for reinforcement learning environments, plus reference environments to test algor

Full review Website

reinforcement-learning-an-introduction

Open source · MIT

Python code that reproduces the figures and examples from the Sutton & Barto reinforcement learning textbook.

Full review Website

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	8 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 gymnasium

Install dependencies for one environment family (example: Atari)

pip install "gymnasium[atari]"

Step-by-step guide: how to use Gymnasium

reinforcement-learning-an-introduction

Clone the repository

git clone https://github.com/ShangtongZhang/reinforcement-learning-an-introduction.git

Step-by-step guide: how to use reinforcement-learning-an-introduction

Frequently 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

reinforcement-learning-an-introduction alternatives Gymnasium vs Stable Baselines3 Gymnasium vs Spinning Up reinforcement-learning-an-introduction vs Stable Baselines3 reinforcement-learning-an-introduction vs Spinning Up

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