RRepoGEO

REPOGEO REPORT · LITE

ShangtongZhang/DeepRL

Default branch master · commit c0968b5c · scanned 6/28/2026, 7:41:52 PM

GitHub: 3,427 stars · 699 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 ShangtongZhang/DeepRL, 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 to highlight its utility as a research/prototyping library

    Why:

    CURRENT
    Modularized implementation of popular deep RL algorithms in PyTorch. Easy switch between toy tasks and challenging games.
    COPY-PASTE FIX
    A modular PyTorch library for deep reinforcement learning, designed for researchers and practitioners to quickly prototype and benchmark various RL algorithms across toy tasks and challenging games.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://your-project-website.com
  • lowtopics#3
    Expand repository topics to include broader category terms

    Why:

    CURRENT
    a2c, categorical-dqn, ddpg, deep-reinforcement-learning, deeprl, double-dqn, dqn, dueling-network-architecture, option-critic, option-critic-architecture, ppo, prioritized-experience-replay, pytorch, quantile-regression, rainbow, td3
    COPY-PASTE FIX
    a2c, categorical-dqn, ddpg, deep-reinforcement-learning, deeprl, double-dqn, dqn, dueling-network-architecture, option-critic, option-critic-architecture, ppo, prioritized-experience-replay, pytorch, quantile-regression, rainbow, td3, reinforcement-learning-framework, rl-library

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 ShangtongZhang/DeepRL
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
CleanRL
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. CleanRL · recommended 2×
  2. Tianshou · recommended 2×
  3. RLlib · recommended 2×
  4. Stable Baselines3 · recommended 2×
  5. TorchRL · recommended 1×
  • CATEGORY QUERY
    Seeking a PyTorch library with modular implementations of various deep reinforcement learning algorithms.
    you: not recommended
    AI recommended (in order):
    1. CleanRL
    2. Tianshou
    3. RLlib
    4. Stable Baselines3
    5. TorchRL

    AI recommended 5 alternatives but never named ShangtongZhang/DeepRL. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are some efficient deep reinforcement learning frameworks for quickly prototyping different RL agents?
    you: not recommended
    AI recommended (in order):
    1. RLlib
    2. Stable Baselines3
    3. CleanRL
    4. Tianshou
    5. Acme

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

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

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ShangtongZhang/DeepRL — 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