RRepoGEO

REPOGEO REPORT · LITE

vmayoral/basic_reinforcement_learning

Default branch master · commit e1e97ff9 · scanned 6/20/2026, 3:27:42 PM

GitHub: 1,218 stars · 368 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
22 /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
1 / 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 vmayoral/basic_reinforcement_learning, 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 clearly state its purpose as a beginner-friendly tutorial series

    Why:

    CURRENT
    Basic Reinforcement Learning (RL)
This repository aims to provide an introduction series to reinforcement learning (RL) by delivering a walkthough on how to code different RL techniques.
    COPY-PASTE FIX
    Basic Reinforcement Learning (RL): A Comprehensive Tutorial Series for Beginners
This repository provides a step-by-step introduction to reinforcement learning (RL) by delivering a walkthough on how to code different RL techniques, ideal for those new to the field.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/vmayoral/basic_reinforcement_learning
  • mediumreadme#3
    Add a dedicated 'Who is this for?' section to the README

    Why:

    COPY-PASTE FIX
    ### Who is this for?
This repository is designed for beginners and students looking for a practical, code-driven introduction to Reinforcement Learning. It's ideal for those who want to understand RL concepts by implementing them step-by-step, rather than just using high-level libraries.

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 vmayoral/basic_reinforcement_learning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Farama-Foundation/Gymnasium
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Farama-Foundation/Gymnasium · recommended 1×
  2. pytorch/pytorch · recommended 1×
  3. tensorflow/tensorflow · recommended 1×
  4. keras-team/keras · recommended 1×
  5. DLR-RM/stable-baselines3 · recommended 1×
  • CATEGORY QUERY
    Seeking a comprehensive step-by-step guide to learn reinforcement learning concepts and implementation.
    you: not recommended
    AI recommended (in order):
    1. Gymnasium (Farama-Foundation/Gymnasium)
    2. PyTorch (pytorch/pytorch)
    3. TensorFlow (tensorflow/tensorflow)
    4. Keras (keras-team/keras)
    5. Stable Baselines3 (DLR-RM/stable-baselines3)
    6. Kaggle RL Competitions

    AI recommended 6 alternatives but never named vmayoral/basic_reinforcement_learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How do I implement deep Q-learning and policy gradient algorithms with practical examples?
    you: not recommended
    AI recommended (in order):
    1. Stable Baselines3
    2. PyTorch
    3. TensorFlow
    4. RLlib
    5. CleanRL
    6. Keras-RL2

    AI recommended 6 alternatives but never named vmayoral/basic_reinforcement_learning. 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 vmayoral/basic_reinforcement_learning?
    pass
    AI did not name vmayoral/basic_reinforcement_learning — 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 vmayoral/basic_reinforcement_learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named vmayoral/basic_reinforcement_learning 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 vmayoral/basic_reinforcement_learning solve, and who is the primary audience?
    pass
    AI did not name vmayoral/basic_reinforcement_learning — 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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vmayoral/basic_reinforcement_learning — 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