OpenAI Gym Retro

https://github.com/openai/retro

OpenAI's Gym Retro — wraps thousands of classic arcade games (Sega Genesis, NES, SNES, Atari 2600, Game Boy) as Gym RL environments via libretro emulators. Released 2018; powered the OpenAI Retro Contest where agents had to generalise from train-set games to held-out levels of Sonic. Largely supplanted by Atari 100k / Procgen / etc. but still the most-popular path for adding arbitrary retro games to RL pipelines.

Archived. repository archived (read-only) Source

Archived · Verified 2026-10-28 · fresh as of 2026-10-28 · 31 cited facts, 1 with a third-party source · 1 recorded change · Suggest a correction

At a glance

Tier
T3
Latest release
not applicable — not OSS fix
License
MIT repo fix
GitHub
3.5k★ Python/C repo fix
Funding
not applicable — not commercial fix
Freshness
2026-10-28 fix
Layer
agent-infra repo fix
Licensing
open-source repo fix

Memory taxonomy

storage
n/a
retrieval
n/a
persistence
n/a
update
read-only
unit
n/a
governance
n/a
conflict
n/a

When to use

Optimised for: not applicable - eval dataset, not a system

Anti-fit: not applicable - eval dataset, not a system

Pros & cons

Pros

Largest catalog of retro games as RL environments (1000+); libretro-backed → broad emulator coverage; canonical for arcade-RL.

Cons

No longer actively maintained by OpenAI (2020 deprecation); ROM legalities limit redistribution; outclassed by Atari 100k / Procgen for generalisation eval.

Claims & capabilities

OpenAI 2018; powered Retro Contest 2018 (Sonic generalisation); 1000+ supported games; canonical entry-point for libretro-emulator RL

Technical surface

API surface
not applicable — eval dataset, not a system
Backend storage
not applicable — eval dataset, not a system
Deployment
not applicable — run locally
Embedding model
not applicable — eval dataset, not a system
Multi-tenancy
not applicable — eval dataset, not a system
MCP
not applicable — benchmark / evaluation harness
A2A
not applicable — benchmark / evaluation harness
OpenTelemetry
not applicable — benchmark / evaluation harness

Similar systems

Other memory benchmarks & evaluation in the catalog, ranked by inbound references.

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  • GAIA T3

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  • LongMemEval T3

    5 core memory abilities — info extraction, multi-session reasoning, temporal reasoning, knowledge updates, abstention — via 500 curated questions in synthetic chat histories. Two scales: LongMemEval-S (115K tokens), LongMemEval-M (up to 1.5M).

  • RULER T3

    NVIDIA. Extends NIAH with multi-needle retrieval, multi-hop entity tracing, aggregation, QA — configurable for arbitrary length / difficulty. Evaluated 17 long-context models on 13 tasks.

  • InfiniteBench (∞Bench) T3

    Tsinghua / OpenBMB. First benchmark with average input >100K tokens. 12 tasks across English + Chinese: long novel QA, code debugging, math in long context, summarisation, retrieval. Realistic documents (novels, code) rather than synthetic filler.

Sources (30 cited facts, 1 third-party)

Each fact links to the page it came from; the badge says who published it (how to read sources).

What changed

Changes to this record's lifecycle, licence, release, pricing and hosting facts, from the catalog's git history. Subscribe (Atom)

  1. 2026-10-22
    • Lifecycle first recorded as “archived”
    #195