Gymnasium vs Spinning Up
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
Free educational guide and short reference code for learning deep reinforcement learning.
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 Spinning Up if you need…
- Students who want a structured entry point into deep RL
- Developers who want readable, standalone implementations of key algorithms
- Researchers looking for a curated reading list of important papers
- People who prefer learning by reading code alongside explanations
Side-by-side
| Gymnasium | Spinning Up | |
|---|---|---|
| Type | Library | Learning resource |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 12.8k | 12k |
| 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.
Spinning Up
Spinning Up is an educational resource from OpenAI for learning deep reinforcement learning, where agents learn tasks by trial and error combined with deep learning. It bundles an introduction to RL terms and theory, an essay on growing into an RL research role, a curated list of key papers, a documented code repository of short standalone algorithm implementations, and a few warm-up exercises. The README describes the project as in maintenance mode, expecting bug fixes and minor updates.
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
Spinning Up
- Students who want a structured entry point into deep RL
- Developers who want readable, standalone implementations of key algorithms
- Researchers looking for a curated reading list of important papers
- People who prefer learning by reading code alongside explanations
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
Spinning Up
- Free and MIT licensed, produced by OpenAI
- Clear educational structure combining theory, papers, code, and exercises
- Implementations are described as short and standalone, which makes them easy to read
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
Spinning Up
- Readers who need production-ready RL libraries, since the project is maintenance-mode and focused on education
- Complete beginners to machine learning, since the material assumes some deep learning background
- People who want a single maintained framework for large-scale training
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
Spinning Up
- Status is maintenance, so new features and updates are unlikely beyond bug fixes and minor changes
- The README excerpt provided does not describe installation or setup steps
- The last push date is August 2024, so the code may lag current library versions
Getting started
Gymnasium
Install the base library
pip install gymnasiumInstall dependencies for one environment family (example: Atari)
pip install "gymnasium[atari]"Spinning Up
Clone the repository
git clone https://github.com/openai/spinningup.gitFrequently asked questions
What is the difference between Gymnasium and Spinning Up?
Gymnasium: A standard Python interface for reinforcement learning environments, plus reference environments to test algor. Spinning Up: Free educational guide and short reference code for learning deep reinforcement learning. Gymnasium suits researchers comparing reinforcement learning algorithms on the same environments, while Spinning Up suits students who want a structured entry point into deep rl.
Are Gymnasium and Spinning Up free?
Gymnasium: Free and open source. Spinning Up: Free and open source.
Is Gymnasium or Spinning Up more popular?
Gymnasium has more GitHub stars (12.8k) than Spinning Up (12k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to Gymnasium and Spinning Up?
Other options people compare them with include Stable Baselines3.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.