RRepoGEO

REPOGEO REPORT · LITE

Kaixhin/Rainbow

Default branch master · commit 1745b184 · scanned 5/12/2026, 11:47:24 AM

GitHub: 1,672 stars · 293 forks

AI VISIBILITY SCORE
35 /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
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 Kaixhin/Rainbow, 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
    Reposition the README's opening to clarify its role as an algorithm implementation

    Why:

    CURRENT
    Rainbow: Combining Improvements in Deep Reinforcement Learning [[1]](#references).
    COPY-PASTE FIX
    Rainbow: A PyTorch reference implementation combining multiple improvements in Deep Reinforcement Learning [[1]](#references). This project provides a faithful and extensible implementation of the Rainbow algorithm, rather than a general-purpose deep reinforcement learning framework.
  • mediumtopics#2
    Add more specific topics to better categorize the repository

    Why:

    CURRENT
    deep-learning, deep-reinforcement-learning
    COPY-PASTE FIX
    deep-learning, deep-reinforcement-learning, pytorch, dqn, rainbow-algorithm, reinforcement-learning-algorithms, research-implementation
  • lowhomepage#3
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    Add the URL to the original Rainbow paper or a relevant project page (e.g., the URL for reference [1] in the README) as the homepage.

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 Kaixhin/Rainbow
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 2×
  2. DLR-RM/stable-baselines3 · recommended 2×
  3. vwxyzjn/cleanrl · recommended 2×
  4. thu-ml/tianshou · recommended 2×
  5. deepmind/acme · recommended 2×
  • CATEGORY QUERY
    How to implement an agent that combines multiple deep reinforcement learning improvements?
    you: not recommended
    AI recommended (in order):
    1. RLlib (ray-project/ray)
    2. Stable Baselines3 (DLR-RM/stable-baselines3)
    3. CleanRL (vwxyzjn/cleanrl)
    4. Tianshou (thu-ml/tianshou)
    5. Acme (deepmind/acme)
    6. TensorFlow Agents (tensorflow/agents)

    AI recommended 6 alternatives but never named Kaixhin/Rainbow. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework helps with advanced deep reinforcement learning research and experimentation?
    you: not recommended
    AI recommended (in order):
    1. RLlib (ray-project/ray)
    2. Stable Baselines3 (DLR-RM/stable-baselines3)
    3. CleanRL (vwxyzjn/cleanrl)
    4. Acme (deepmind/acme)
    5. Tianshou (thu-ml/tianshou)

    AI recommended 5 alternatives but never named Kaixhin/Rainbow. 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 Kaixhin/Rainbow?
    pass
    AI named Kaixhin/Rainbow explicitly

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

  • If a team adopts Kaixhin/Rainbow in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Kaixhin/Rainbow 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 Kaixhin/Rainbow solve, and who is the primary audience?
    pass
    AI named Kaixhin/Rainbow explicitly

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

Embed your GEO score

Drop this badge into the README of Kaixhin/Rainbow. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/Kaixhin/Rainbow.svg)](https://repogeo.com/en/r/Kaixhin/Rainbow)
HTML
<a href="https://repogeo.com/en/r/Kaixhin/Rainbow"><img src="https://repogeo.com/badge/Kaixhin/Rainbow.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

Kaixhin/Rainbow — 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