RRepoGEO

REPOGEO REPORT · LITE

floodsung/DDPG

Default branch master · commit 18825eed · scanned 6/2/2026, 11:17:45 AM

GitHub: 576 stars · 177 forks

AI VISIBILITY SCORE
35 /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
3 / 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 floodsung/DDPG, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README's opening to clarify project scope

    Why:

    CURRENT
    # DDPG

Reimplementing DDPG from Continuous Control with Deep Reinforcement Learning based on OpenAI Gym and Tensorflow
    COPY-PASTE FIX
    # DDPG: A Research Implementation of Deep Deterministic Policy Gradient

This repository provides a focused re-implementation of the Deep Deterministic Policy Gradient (DDPG) algorithm, as described in 'Continuous Control with Deep Reinforcement Learning', using OpenAI Gym and TensorFlow. It serves as a practical example for understanding and experimenting with DDPG for continuous control problems.
  • mediumhomepage#2
    Add the original paper's URL as the homepage

    Why:

    COPY-PASTE FIX
    http://arxiv.org/abs/1509.02971

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 floodsung/DDPG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
RLlib
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. RLlib · recommended 2×
  2. TF-Agents · recommended 2×
  3. Keras-RL2 · recommended 2×
  4. Stable Baselines3 · recommended 1×
  5. Stable Baselines3 (SB3) · recommended 1×
  • CATEGORY QUERY
    How can I implement continuous control reinforcement learning using DDPG with OpenAI Gym?
    you: not recommended
    AI recommended (in order):
    1. Stable Baselines3
    2. RLlib
    3. TF-Agents
    4. Keras-RL2

    AI recommended 4 alternatives but never named floodsung/DDPG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python libraries are available for DDPG with TensorFlow, including batch normalization support?
    you: not recommended
    AI recommended (in order):
    1. TF-Agents
    2. Stable Baselines3 (SB3)
    3. Keras-RL2
    4. RLlib

    AI recommended 4 alternatives but never named floodsung/DDPG. 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 floodsung/DDPG?
    pass
    AI named floodsung/DDPG explicitly

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

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

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

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MARKDOWN (README)
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floodsung/DDPG — 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