RRepoGEO

REPOGEO REPORT · LITE

sfujim/TD3

Default branch master · commit 34770ccd · scanned 6/21/2026, 8:02:51 PM

GitHub: 2,087 stars · 481 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
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 sfujim/TD3, 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
  • mediumhomepage#1
    Add the paper's URL as the repository homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/1802.09477
  • lowreadme#2
    Add a dedicated 'Citation' section to the README

    Why:

    COPY-PASTE FIX
    ## Citation
    
    If you use our code or data, please cite the paper:
    
    ```bibtex
    @article{fujimoto2018addressing,
      title={Addressing Function Approximation Error in Actor-Critic Methods},
      author={Fujimoto, Scott and van Hoof, Herke and Precup, Doina},
      journal={arXiv preprint arXiv:1802.09477},
      year={2018}
    }
    ```

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 sfujim/TD3
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Soft Actor-Critic (SAC)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Soft Actor-Critic (SAC) · recommended 1×
  2. Twin Delayed DDPG (TD3) · recommended 1×
  3. Proximal Policy Optimization (PPO) · recommended 1×
  4. Deep Deterministic Policy Gradient (DDPG) · recommended 1×
  5. Asynchronous Advantage Actor-Critic (A3C) · recommended 1×
  • CATEGORY QUERY
    What are effective deep reinforcement learning algorithms for continuous control with PyTorch?
    you: not recommended
    AI recommended (in order):
    1. Soft Actor-Critic (SAC)
    2. Twin Delayed DDPG (TD3)
    3. Proximal Policy Optimization (PPO)
    4. Deep Deterministic Policy Gradient (DDPG)
    5. Asynchronous Advantage Actor-Critic (A3C)
    6. Trust Region Policy Optimization (TRPO)

    AI recommended 6 alternatives but never named sfujim/TD3. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which deep actor-critic methods improve stability for continuous action space problems?
    you: not recommended
    AI recommended (in order):
    1. TD3 (Twin Delayed DDPG)
    2. SAC (Soft Actor-Critic)
    3. DDPG (Deep Deterministic Policy Gradient)
    4. PPO (Proximal Policy Optimization)
    5. A2C (Advantage Actor-Critic)
    6. TRPO (Trust Region Policy Optimization)

    AI recommended 6 alternatives but never named sfujim/TD3. 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 sfujim/TD3?
    pass
    AI named sfujim/TD3 explicitly

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

  • If a team adopts sfujim/TD3 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named sfujim/TD3 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 sfujim/TD3 solve, and who is the primary audience?
    pass
    AI named sfujim/TD3 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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Pro

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sfujim/TD3 — 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
sfujim/TD3 — RepoGEO report