openai/retro

Health v2 · maintenance only

Flags: archived

Adoption not part of the score

3585 stars · 535 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Gym Retro is a Python library that turns classic video games into Gym environments for reinforcement learning research, with integrations for ~1000 games via Libretro emulators. It includes memory locations, reward functions, and savestates for each game integration, though ROMs must be supplied by the user.

Use cases

• train reinforcement learning agents on retro video games
• benchmark RL generalization across game levels
• create Gym environments from emulated games
• research reward functions from in-game memory variables
• run RL experiments on Atari, NES, SNES, Genesis, and Game Boy games

When to choose

• you need standardized RL environments based on classic games
• you want reproducible game integrations with savestates and reward definitions
• you need a benchmark for generalization in reinforcement learning

When to avoid

• you need actively developed features or support for recent Python versions
• you want a general-purpose game emulator for playing games
• you expect ROMs to be bundled with the library

Facets

library · maturity maintenance

reinforcement-learning simulation game-engine sdk reinforcement-learning machine-learning artificial-intelligence python windows cpp c gym-environments emulation libretro retro-games rl-benchmark game-development linux macos

Member repositories

For agents

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