RRepoGEO

REPOGEO REPORT · LITE

Curt-Park/rainbow-is-all-you-need

Default branch master · commit e864f0be · scanned 5/27/2026, 10:28:30 PM

GitHub: 2,024 stars · 353 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 Curt-Park/rainbow-is-all-you-need, 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 emphasize its educational purpose

    Why:

    CURRENT
    This is a step-by-step tutorial from DQN to Rainbow. Every chapter contains both of theoretical backgrounds and object-oriented implementation. Just pick any topic in which you are interested, and learn! You can run them directly in the cloud with molab — no local setup needed.
    COPY-PASTE FIX
    This repository offers a comprehensive, step-by-step tutorial from DQN to Rainbow, designed as an educational resource for learning deep reinforcement learning concepts and implementations. It is not a production-ready library. Every chapter contains both theoretical backgrounds and object-oriented implementation. Just pick any topic in which you are interested, and learn! You can run them directly in the cloud with molab — no local setup needed.
  • highhomepage#2
    Add a homepage link to the main interactive tutorial

    Why:

    COPY-PASTE FIX
    https://nbviewer.org/github/Curt-Park/rainbow-is-all-you-need/blob/main/01_dqn.ipynb
  • mediumtopics#3
    Add explicit tutorial-related topics

    Why:

    CURRENT
    colab-notebook, dqn, gym-environment, nbviewer, pytorch, rainbow, reinforcement-learning
    COPY-PASTE FIX
    colab-notebook, dqn, gym-environment, nbviewer, pytorch, rainbow, reinforcement-learning, tutorial, deep-q-learning-tutorial, educational-resource

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 Curt-Park/rainbow-is-all-you-need
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Stable Baselines3
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Stable Baselines3 · recommended 2×
  2. PyTorch · recommended 1×
  3. OpenAI Gym · recommended 1×
  4. Spinning Up in Deep RL · recommended 1×
  5. TensorFlow · recommended 1×
  • CATEGORY QUERY
    Seeking a comprehensive tutorial to understand and implement advanced deep Q-learning algorithms.
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. OpenAI Gym
    3. Spinning Up in Deep RL
    4. TensorFlow
    5. Stable Baselines3
    6. PyTorch Reinforcement Learning Tutorials

    AI recommended 6 alternatives but never named Curt-Park/rainbow-is-all-you-need. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to implement Rainbow reinforcement learning in PyTorch with practical examples?
    you: not recommended
    AI recommended (in order):
    1. Stable Baselines3
    2. RLlib
    3. CleanRL
    4. Minigrid-DQN
    5. PyTorch-RL

    AI recommended 5 alternatives but never named Curt-Park/rainbow-is-all-you-need. 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 Curt-Park/rainbow-is-all-you-need?
    pass
    AI named Curt-Park/rainbow-is-all-you-need explicitly

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

  • If a team adopts Curt-Park/rainbow-is-all-you-need in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Curt-Park/rainbow-is-all-you-need 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 Curt-Park/rainbow-is-all-you-need solve, and who is the primary audience?
    pass
    AI did not name Curt-Park/rainbow-is-all-you-need — 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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Curt-Park/rainbow-is-all-you-need — 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