Retro
Play classic games in Gym for reinforcement learning.
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is Retro?
Retro provides a collection of classic video game environments for use with the OpenAI Gym framework, enabling developers to train and test reinforcement learning algorithms on these games.
Key differentiator
“Retro stands out as a specialized tool within the OpenAI Gym ecosystem, offering a unique collection of classic game environments tailored specifically for reinforcement learning research and education.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
Retro primarily supports classic video game environments which may not be suitable for developing advanced reinforcement learning algorithms.
Games with high-resolution graphics or complex state spaces can lead to slow performance and increased computational requirements.
The documentation focuses on basic setup and usage, but does not provide detailed guidance for more sophisticated reinforcement learning tasks or custom game environments.
Fit analysis
Who is it for?
✓ Best for
Researchers looking to benchmark their reinforcement learning models against classic game environments.
Students and educators who want to teach reinforcement learning using familiar video games.
✕ Not a fit for
Developers needing real-time interaction with the environment for applications like robotics or autonomous vehicles.
Teams working on large-scale distributed training systems that require cloud-based solutions.
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Next step
Get Started with Retro
Step-by-step setup guide with code examples and common gotchas.