RRepoGEO

REPOGEO REPORT · LITE

Kaixhin/Rainbow

Default branch master · commit 1745b184 · scanned 6/22/2026, 5:57:24 PM

GitHub: 1,673 stars · 294 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 a reference implementation

    Why:

    CURRENT
    Rainbow: Combining Improvements in Deep Reinforcement Learning [[1]](#references).
    COPY-PASTE FIX
    Rainbow: A PyTorch reference implementation combining key improvements in Deep Reinforcement Learning, as detailed in the original paper [[1]](#references). This repository provides a robust and reproducible codebase for researchers and practitioners to explore and build upon the Rainbow DQN algorithm.
  • mediumhomepage#2
    Add the paper's URL as the repository homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/1710.02298
  • lowtopics#3
    Add 'pytorch-implementation' to the repository topics

    Why:

    CURRENT
    deep-learning, deep-reinforcement-learning
    COPY-PASTE FIX
    deep-learning, deep-reinforcement-learning, pytorch-implementation

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
Proximal Policy Optimization (PPO)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Proximal Policy Optimization (PPO) · recommended 1×
  2. Soft Actor-Critic (SAC) · recommended 1×
  3. Deep Q-Networks (DQN) · recommended 1×
  4. Double DQN · recommended 1×
  5. Dueling DQN · recommended 1×
  • CATEGORY QUERY
    What are the best deep reinforcement learning algorithms for improved agent performance?
    you: not recommended
    AI recommended (in order):
    1. Proximal Policy Optimization (PPO)
    2. Soft Actor-Critic (SAC)
    3. Deep Q-Networks (DQN)
    4. Double DQN
    5. Dueling DQN
    6. Prioritized Experience Replay DQN
    7. Rainbow DQN
    8. Twin Delayed DDPG (TD3)
    9. DDPG (Deep Deterministic Policy Gradient)
    10. Asynchronous Advantage Actor-Critic (A3C)
    11. Recurrent Differentiable Attention (RDA)
    12. AlphaZero
    13. MuZero

    AI recommended 13 alternatives but never named Kaixhin/Rainbow. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to implement advanced deep Q-network enhancements for faster learning and stability?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Baselines (openai/baselines)
    2. Stable Baselines3 (DLR-RM/stable-baselines3)
    3. Keras-RL (keras-rl/keras-rl)
    4. RLlib (ray-project/ray)
    5. Acme (deepmind/acme)

    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