openai/procgen

Health v2 · maintenance only

Adoption not part of the score

1191 stars · 224 forks observed · 2026-09-24

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A benchmark of 16 procedurally-generated, game-like Gym environments for measuring how quickly reinforcement learning agents learn generalizable skills. Environments run at thousands of steps per second on a single core and are customizable when built from source.

Use cases

• benchmark reinforcement learning generalization
• measure RL sample efficiency
• train RL agents on procedurally generated levels
• run fast gym environments for RL research
• compare RL agents against overfitting to fixed levels
• build custom RL environments from source

When to choose

• You need fast, randomized Gym environments to test RL generalization
• You want a standard benchmark comparable to published Procgen results
• You need environments that are easy to modify for RL experiments

When to avoid

• You need actively developed features or new environments
• You require GPU-accelerated or vectorized-only environments
• You need environments beyond Python 3.10 or without AVX CPU support

Facets

library · maturity maintenance

simulation machine-learning reinforcement-learning game-engine reinforcement-learning machine-learning gaming-tools python windows cpp gym-environments procedural-generation rl-benchmark openai research linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="openai/procgen")