RRepoGEO

REPOGEO REPORT · LITE

FareedKhan-dev/all-rl-algorithms

Default branch master · commit 6989b342 · scanned 6/20/2026, 2:07:32 PM

GitHub: 1,782 stars · 323 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
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 FareedKhan-dev/all-rl-algorithms, 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
  • highabout#1
    Refine the 'About' description to emphasize educational, from-scratch focus

    Why:

    CURRENT
    Implementation of all RL algorithms in a simpler way
    COPY-PASTE FIX
    Educational implementations of core Reinforcement Learning (RL) algorithms from scratch, prioritizing clarity and understanding over performance.
  • hightopics#2
    Update topics to remove misleading ones and add educational keywords

    Why:

    CURRENT
    agent, llm, openai, python, reinforcement-learning, rl
    COPY-PASTE FIX
    reinforcement-learning, rl, python, education, learning, algorithms-from-scratch, deep-learning-algorithms
  • mediumreadme#3
    Strengthen the README's opening sentence to immediately convey educational purpose

    Why:

    CURRENT
    This repository is a collection of Python implementations of various Reinforcement Learning (RL) algorithms.
    COPY-PASTE FIX
    This repository offers **educational, from-scratch Python implementations** of various Reinforcement Learning (RL) algorithms, designed for deep understanding rather than production use.

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 FareedKhan-dev/all-rl-algorithms
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Stable Baselines3 · recommended 1×
  2. Spinning Up in Deep RL · recommended 1×
  3. RLlib · recommended 1×
  4. Deep Reinforcement Learning Hands-On · recommended 1×
  5. TensorFlow Agents · recommended 1×
  • CATEGORY QUERY
    How can I learn core reinforcement learning algorithms with clear Python implementations?
    you: not recommended
    AI recommended (in order):
    1. Stable Baselines3
    2. Spinning Up in Deep RL
    3. RLlib
    4. Deep Reinforcement Learning Hands-On
    5. TensorFlow Agents
    6. Keras-RL2

    AI recommended 6 alternatives but never named FareedKhan-dev/all-rl-algorithms. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find simple, readable Python examples for fundamental RL concepts?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Spinning Up (openai/spinningup)
    2. Stable Baselines3 (DLR-RM/stable-baselines3)
    3. PyTorch (pytorch/pytorch)
    4. TensorFlow Agents (tensorflow/agents)

    AI recommended 4 alternatives but never named FareedKhan-dev/all-rl-algorithms. 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 FareedKhan-dev/all-rl-algorithms?
    pass
    AI named FareedKhan-dev/all-rl-algorithms explicitly

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

  • If a team adopts FareedKhan-dev/all-rl-algorithms in production, what risks or prerequisites should they evaluate first?
    pass
    AI named FareedKhan-dev/all-rl-algorithms 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 FareedKhan-dev/all-rl-algorithms solve, and who is the primary audience?
    pass
    AI did not name FareedKhan-dev/all-rl-algorithms — 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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FareedKhan-dev/all-rl-algorithms — 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