RRepoGEO

REPOGEO REPORT · LITE

dusty-nv/jetson-reinforcement

Default branch master · commit 2974ef71 · scanned 6/15/2026, 4:07:11 PM

GitHub: 931 stars · 224 forks

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 dusty-nv/jetson-reinforcement, 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 H1 to specify the target platform and purpose

    Why:

    CURRENT
    # Deep Reinforcement Learning in Robotics
    COPY-PASTE FIX
    # Deep Reinforcement Learning GPU Libraries for NVIDIA Jetson Robotics
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    deep-reinforcement-learning, robotics, nvidia-jetson, pytorch, openai-gym, gazebo, embedded-systems, gpu-acceleration, ai-robotics
  • mediumreadme#3
    Add a clarifying sentence after the H1 to frame the project's specific value

    Why:

    COPY-PASTE FIX
    This repository provides foundational deep reinforcement learning GPU libraries specifically for NVIDIA Jetson TX1/TX2, demonstrating core concepts for embedded robotics.

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 dusty-nv/jetson-reinforcement
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 1×
  2. TorchScript · recommended 1×
  3. PyTorch Mobile · recommended 1×
  4. TensorFlow Lite · recommended 1×
  5. TensorFlow · recommended 1×
  • CATEGORY QUERY
    Libraries for deep reinforcement learning in robotics, suitable for embedded GPU platforms?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TorchScript
    3. PyTorch Mobile
    4. TensorFlow Lite
    5. TensorFlow
    6. ONNX Runtime
    7. NVIDIA TensorRT
    8. RLlib
    9. Stable Baselines3

    AI recommended 9 alternatives but never named dusty-nv/jetson-reinforcement. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Toolkits for developing reinforcement learning models in simulation for real-world robot deployment?
    you: not recommended
    AI recommended (in order):
    1. Isaac Sim
    2. PyBullet
    3. MuJoCo
    4. Gazebo
    5. Webots
    6. ROS 2 with Gazebo/Ignition
    7. CoppeliaSim

    AI recommended 7 alternatives but never named dusty-nv/jetson-reinforcement. 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 dusty-nv/jetson-reinforcement?
    pass
    AI named dusty-nv/jetson-reinforcement explicitly

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

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

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