RRepoGEO

REPOGEO REPORT · LITE

Farama-Foundation/D4RL

Default branch master · commit 89141a68 · scanned 5/8/2026, 7:12:27 PM

GitHub: 1,678 stars · 304 forks

AI VISIBILITY SCORE
88 /100
Healthy
Category recall
2 / 2
Avg rank #1.0 when recommended
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface Farama-Foundation/D4RL, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • mediumhomepage#1
    Add the project's official homepage URL

    Why:

    COPY-PASTE FIX
    [Insert the URL of the supplementary website mentioned in the README, e.g., "https://www.d4rl.ai"]
  • lowreadme#2
    Refine README's initial description to emphasize current focus

    Why:

    CURRENT
    D4RL is an open-source benchmark for offline reinforcement learning. It provides standardized environments and datasets for training and benchmarking algorithms.
    COPY-PASTE FIX
    D4RL serves as a foundational open-source benchmark, providing standardized datasets for offline reinforcement learning. It enables researchers to train and evaluate algorithms against a consistent collection of pre-recorded data.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
2 / 2
100% of queries surface Farama-Foundation/D4RL
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
18%
Of all named tools, what % are you?
Top rival
openai/gym
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. openai/gym · recommended 2×
  2. rlworkgroup/metaworld · recommended 1×
  3. RoboStack/robostack · recommended 1×
  4. bulletphysics/bullet3 · recommended 1×
  5. deepmind/rl_unplugged · recommended 1×
  • CATEGORY QUERY
    What are good reference environments for training offline RL agents?
    you: #1
    AI recommended (in order):
    1. D4RL (rail-berkeley/d4rl) ← you
    2. OpenAI Gym (openai/gym)
    3. Meta-World (rlworkgroup/metaworld)
    4. RoboStack (RoboStack/robostack)
    5. PyBullet (bulletphysics/bullet3)
    Show full AI answer
  • CATEGORY QUERY
    Where can I find standardized datasets for offline reinforcement learning algorithm benchmarking?
    you: #1
    AI recommended (in order):
    1. D4RL (rail-berkeley/d4rl) ← you
    2. RL Unplugged (deepmind/rl_unplugged)
    3. Open X-Embodiment (OXE) Dataset
    4. RoboMimic (ARISE-Initiative/robomimic)
    5. Behavioral Cloning (BC) datasets (openai/gym)
    6. Atari 2600 datasets (mgbellemare/Arcade-Learning-Environment)
    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of Farama-Foundation/D4RL?
    pass
    AI named Farama-Foundation/D4RL explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts Farama-Foundation/D4RL in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Farama-Foundation/D4RL explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo Farama-Foundation/D4RL solve, and who is the primary audience?
    pass
    AI named Farama-Foundation/D4RL explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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Farama-Foundation/D4RL — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite