RRepoGEO

REPOGEO REPORT · LITE

tambetm/simple_dqn

Default branch master · commit e5669520 · scanned 6/4/2026, 12:57:58 PM

GitHub: 702 stars · 185 forks

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 tambetm/simple_dqn, 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 statement to clarify educational purpose

    Why:

    CURRENT
    **Unfortunately this repo is outdated and there are much better codebases out there. I would suggest to take a look at this to learn the basics or this for full-blown DQN implementation for Atari.**
    COPY-PASTE FIX
    This repository provides a clear, foundational implementation of Deep Q-learning for educational purposes, demonstrating the core concepts from DeepMind's 'Human-level control through deep reinforcement learning' paper. It is designed to be simple, fast, and easy to extend for those learning the basics of DQN.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    deep-q-learning, reinforcement-learning, dqn, machine-learning, python, atari, openai-gym
  • mediumreadme#3
    Clarify the role of the Neon library in the README's feature list

    Why:

    CURRENT
    Fastest convolutions from Neon deep learning library.
    COPY-PASTE FIX
    Utilizes the Neon deep learning library for efficient convolutions, offering a historical example of early DQN implementations.

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 tambetm/simple_dqn
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. RLlib · recommended 2×
  3. PyTorch · recommended 1×
  4. OpenAI Gym · recommended 1×
  5. Gymnasium · recommended 1×
  • CATEGORY QUERY
    How to implement deep Q-learning agents for reinforcement learning in Python?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. Stable Baselines3
    3. OpenAI Gym
    4. Gymnasium
    5. TensorFlow
    6. Keras
    7. TF-Agents
    8. RLlib
    9. Ray

    AI recommended 9 alternatives but never named tambetm/simple_dqn. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a simple and fast Python library for deep reinforcement learning research.
    you: not recommended
    AI recommended (in order):
    1. Stable Baselines3
    2. CleanRL
    3. RLlib
    4. Tianshou
    5. Acme

    AI recommended 5 alternatives but never named tambetm/simple_dqn. 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 tambetm/simple_dqn?
    pass
    AI did not name tambetm/simple_dqn — 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 tambetm/simple_dqn in production, what risks or prerequisites should they evaluate first?
    pass
    AI named tambetm/simple_dqn 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 tambetm/simple_dqn solve, and who is the primary audience?
    pass
    AI did not name tambetm/simple_dqn — 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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tambetm/simple_dqn — 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