Gymnasium vs Stable-Baselines3

Side-by-side — pricing, strengths, tradeoffs, and fit.

Gymnasium

A library for developing and comparing reinforcement learning algorithms.

Stable-Baselines3

PyTorch-based reinforcement learning algorithms for deep RL research and applications.

At a glance

Open SourceFree Tier
● Established·From $0
Open SourceFree Tier
● Established·From $0

Overview

One-liner

A library for developing and comparing reinforcement learning algorithms.

One-liner

PyTorch-based reinforcement learning algorithms for deep RL research and applications.

Maturity

Established

Maturity

Established

Open source

✓ Yes

Open source

✓ Yes

Pricing

Free tier

✓ Available

Open source — free to use

Free tier

✓ Available

Open source — free to use

Starts at

$0

Starts at

$0

Model

Flat rate

Model

Flat rate

Enterprise

✕ None

Enterprise

✕ None

Best for

✓ Best for

Academics and researchers who need a robust framework for testing RL algorithms.
Developers working on projects that require reinforcement learning environments.

✓ Best for

Researchers looking to implement and test deep RL algorithms with PyTorch
Developers building autonomous systems that require reinforcement learning techniques
Academics who need a reliable framework for teaching and research in reinforcement learning

Not ideal for

✕ Not for

Projects requiring real-time interaction with physical systems (Gymnasium is more suited for simulation).
Applications where the overhead of setting up an environment outweighs the benefits.

✕ Not for

Projects requiring real-time performance critical applications where stability is less important than speed
Teams looking for a cloud-based managed service for reinforcement learning

Strengths

↑ Strengths

Successor to OpenAI Gym, with improved features and support.
Extensive library of environments for testing reinforcement learning algorithms.
Community-driven development and maintenance.

↑ Strengths

PyTorch-based implementations of reinforcement learning algorithms
Stability and reliability in deep RL research
Extensive documentation and examples for quick start

Weaknesses

↓ Weaknesses

Limited documentation for advanced use cases
Performance bottlenecks in large-scale simulations

↓ Weaknesses

Frequent breaking changes between versions
Limited support for non-PyTorch frameworks
Resource-intensive for large-scale applications
Full Gymnasium profile →Full Stable-Baselines3 profile →