RRepoGEO

REPOGEO REPORT · LITE

germain-hug/Deep-RL-Keras

Default branch master · commit a50cc30b · scanned 6/7/2026, 9:08:04 AM

GitHub: 550 stars · 146 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 germain-hug/Deep-RL-Keras, 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 highlight Keras and modularity

    Why:

    CURRENT
    Keras Implementation of popular Deep RL Algorithms (A3C, DDQN, DDPG, Dueling DDQN)
    COPY-PASTE FIX
    Modular Keras implementations of popular Deep Reinforcement Learning algorithms (A3C, DDQN, DDPG, Dueling DDQN), ideal for research, learning, and rapid prototyping.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Add a link to the repository itself, a documentation site, or a project page as the homepage URL in the repository settings.

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 germain-hug/Deep-RL-Keras
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DLR-RM/stable-baselines3
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. DLR-RM/stable-baselines3 · recommended 1×
  2. ray-project/ray · recommended 1×
  3. vwxyzjn/cleanrl · recommended 1×
  4. thu-ml/tianshou · recommended 1×
  5. deepmind/acme · recommended 1×
  • CATEGORY QUERY
    Need a library for implementing various deep reinforcement learning algorithms efficiently.
    you: not recommended
    AI recommended (in order):
    1. Stable Baselines3 (DLR-RM/stable-baselines3)
    2. RLlib (ray-project/ray)
    3. CleanRL (vwxyzjn/cleanrl)
    4. Tianshou (thu-ml/tianshou)
    5. Acme (deepmind/acme)
    6. Catalyst.RL (catalyst-team/catalyst)
    7. Dopamine (google/dopamine)

    AI recommended 7 alternatives but never named germain-hug/Deep-RL-Keras. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find modular deep reinforcement learning implementations for research?
    you: not recommended
    AI recommended (in order):
    1. CleanRL
    2. RLlib
    3. Stable Baselines3
    4. Tianshou
    5. Acme
    6. Catalyst.RL

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

Embed your GEO score

Drop this badge into the README of germain-hug/Deep-RL-Keras. 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/germain-hug/Deep-RL-Keras.svg)](https://repogeo.com/en/r/germain-hug/Deep-RL-Keras)
HTML
<a href="https://repogeo.com/en/r/germain-hug/Deep-RL-Keras"><img src="https://repogeo.com/badge/germain-hug/Deep-RL-Keras.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

germain-hug/Deep-RL-Keras — 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