RRepoGEO

REPOGEO REPORT · LITE

seungeunrho/minimalRL

Default branch master · commit c8efed84 · scanned 6/28/2026, 10:32:52 AM

GitHub: 3,208 stars · 493 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
59 /100
Needs work
Category recall
1 / 2
Avg rank #2.0 when recommended
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 seungeunrho/minimalRL, 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 line to emphasize quick experimentation

    Why:

    CURRENT
    Implementations of basic RL algorithms with minimal lines of codes! (PyTorch based)
    COPY-PASTE FIX
    A collection of lightweight, single-file PyTorch implementations of core Reinforcement Learning algorithms, designed for quick understanding and rapid experimentation.
  • mediumabout#2
    Update the About section description to highlight experimentation

    Why:

    CURRENT
    Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)
    COPY-PASTE FIX
    Lightweight, single-file PyTorch implementations of core RL algorithms, perfect for quick understanding and rapid experimentation.
  • lowhomepage#3
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/seungeunrho/minimalRL

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
1 / 2
50% of queries surface seungeunrho/minimalRL
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
9%
Of all named tools, what % are you?
Top rival
p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch · recommended 1×
  2. PacktPublishing/Deep-Reinforcement-Learning-in-Action · recommended 1×
  3. PyTorch official examples · recommended 1×
  4. vwxyzjn/cleanrl · recommended 1×
  5. higgsfield/RL-Adventure · recommended 1×
  • CATEGORY QUERY
    Looking for simple PyTorch implementations of core reinforcement learning algorithms for quick understanding.
    you: #2
    AI recommended (in order):
    1. PyTorch Reinforcement Learning (PyTorch-RL) (p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch)
    2. Minimal RL (seungeunrho/minimalRL) ← you
    3. Deep Reinforcement Learning in Action (PacktPublishing/Deep-Reinforcement-Learning-in-Action)
    4. PyTorch official examples
    5. CleanRL (vwxyzjn/cleanrl)
    6. RL-Adventure (higgsfield/RL-Adventure)
    Show full AI answer
  • CATEGORY QUERY
    What are some lightweight PyTorch libraries for quickly experimenting with basic RL algorithms?
    you: not recommended
    AI recommended (in order):
    1. CleanRL
    2. RLlib
    3. Tianshou
    4. Stable Baselines3
    5. Minigrid

    AI recommended 5 alternatives but never named seungeunrho/minimalRL. 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 seungeunrho/minimalRL?
    pass
    AI did not name seungeunrho/minimalRL — 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?

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

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

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seungeunrho/minimalRL — 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