RRepoGEO

REPOGEO REPORT · LITE

aikorea/awesome-rl

Default branch master · commit 774cb664 · scanned 5/15/2026, 1:48:07 PM

GitHub: 9,759 stars · 1,914 forks

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 aikorea/awesome-rl, 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

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

OVERALL DIRECTION
  • highreadme#1
    Remove 'no longer maintained' statement from README

    Why:

    CURRENT
    This page is no longer maintained.
    COPY-PASTE FIX
    Remove the line 'This page is no longer maintained.' from the README.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    reinforcement-learning, rl, awesome-list, curated-list, machine-learning, deep-learning, ai
  • mediumlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Add a LICENSE file (e.g., MIT License) to the repository root.

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
0 / 2
0% of queries surface aikorea/awesome-rl
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI Spinning Up in Deep RL
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI Spinning Up in Deep RL · recommended 1×
  2. Deep Reinforcement Learning Hands-On · recommended 1×
  3. Reinforcement Learning: An Introduction · recommended 1×
  4. DeepMind's AlphaGo and AlphaZero Papers/Blogs · recommended 1×
  5. ray-project/ray · recommended 1×
  • CATEGORY QUERY
    What are the best curated resources for learning and applying reinforcement learning algorithms?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Spinning Up in Deep RL
    2. Deep Reinforcement Learning Hands-On
    3. Reinforcement Learning: An Introduction
    4. DeepMind's AlphaGo and AlphaZero Papers/Blogs
    5. RLlib (ray-project/ray)
    6. Stable Baselines3 (DLR-RM/stable-baselines3)
    7. David Silver's Reinforcement Learning Course

    AI recommended 7 alternatives but never named aikorea/awesome-rl. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for open-source platforms and code implementations for various reinforcement learning applications.
    you: not recommended
    AI recommended (in order):
    1. RLlib
    2. Stable Baselines3
    3. Tianshou
    4. CleanRL
    5. Dopamine
    6. Acme
    7. Gymnasium

    AI recommended 7 alternatives but never named aikorea/awesome-rl. This is the gap to close.

    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 aikorea/awesome-rl?
    pass
    AI named aikorea/awesome-rl explicitly

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

  • If a team adopts aikorea/awesome-rl in production, what risks or prerequisites should they evaluate first?
    pass
    AI named aikorea/awesome-rl 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 aikorea/awesome-rl solve, and who is the primary audience?
    pass
    AI did not name aikorea/awesome-rl — likely talking about a different project

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

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aikorea/awesome-rl — 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