RRepoGEO

REPOGEO REPORT · LITE

coreylynch/async-rl

Default branch master · commit 1741d52c · scanned 5/16/2026, 9:18:23 PM

GitHub: 1,006 stars · 169 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
28 /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
2 / 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 coreylynch/async-rl, 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
    Clarify the specific algorithm (1-step Q Learning) and differentiate from A3C in the README's opening

    Why:

    CURRENT
    This is a Tensorflow + Keras implementation of asyncronous 1-step Q learning as described in "Asynchronous Methods for Deep Reinforcement Learning".
    COPY-PASTE FIX
    This is a Tensorflow + Keras implementation of **asynchronous 1-step Q learning**, as described in "Asynchronous Methods for Deep Reinforcement Learning". **Note: This project implements 1-step Q learning, not A3C (Asynchronous Advantage Actor-Critic), which is a different algorithm from the same paper.**
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    tensorflow, keras, openai-gym, reinforcement-learning, deep-q-learning, asynchronous-rl
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/coreylynch/async-rl

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 coreylynch/async-rl
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 2×
  2. Proximal Policy Optimization (PPO) · recommended 1×
  3. Advantage Actor-Critic (A2C) · recommended 1×
  4. Asynchronous Advantage Actor-Critic (A3C) · recommended 1×
  5. Trust Region Policy Optimization (TRPO) · recommended 1×
  • CATEGORY QUERY
    Seeking a deep reinforcement learning approach that avoids memory-intensive experience replay.
    you: not recommended
    AI recommended (in order):
    1. Proximal Policy Optimization (PPO)
    2. Advantage Actor-Critic (A2C)
    3. Asynchronous Advantage Actor-Critic (A3C)
    4. Trust Region Policy Optimization (TRPO)
    5. REINFORCE
    6. Soft Actor-Critic (SAC)
    7. Evolution Strategies (ES)

    AI recommended 7 alternatives but never named coreylynch/async-rl. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks support concurrent agent training for deep reinforcement learning tasks?
    you: not recommended
    AI recommended (in order):
    1. Ray RLlib (ray-project/ray)
    2. Ray (ray-project/ray)
    3. OpenSpiel (deepmind/open_spiel)
    4. Acme (deepmind/acme)
    5. Tianshou (thu-ml/tianshou)
    6. CleanRL (vwxyzjn/cleanrl)
    7. Gymnasium (Farama-Foundation/Gymnasium)
    8. OpenAI Gym (openai/gym)
    9. Stable Baselines3 (DLR-RM/stable-baselines3)

    AI recommended 9 alternatives but never named coreylynch/async-rl. 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 coreylynch/async-rl?
    pass
    AI named coreylynch/async-rl explicitly

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

  • If a team adopts coreylynch/async-rl in production, what risks or prerequisites should they evaluate first?
    pass
    AI named coreylynch/async-rl 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 coreylynch/async-rl solve, and who is the primary audience?
    pass
    AI did not name coreylynch/async-rl — 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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coreylynch/async-rl — 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