Retro
Play classic games in Gym for reinforcement learning.
Pricing
See website
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
—Overview
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
Strength Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
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
None
Starts at
See website
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Next step
Get Started with Retro
Step-by-step setup guide with code examples and common gotchas.