RRepoGEO

REPOGEO REPORT · LITE

dxyang/DQN_pytorch

Default branch master · commit 43fe371b · scanned 6/8/2026, 6:18:29 PM

GitHub: 564 stars · 97 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 dxyang/DQN_pytorch, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the content of the MIT License (or another suitable open-source license).
  • mediumabout#2
    Update the repository's 'About' description

    Why:

    CURRENT
    Vanilla DQN, Double DQN, and Dueling DQN implemented in PyTorch
    COPY-PASTE FIX
    PyTorch implementations of Vanilla DQN, Double DQN, and Dueling DQN, ideal for students and researchers learning and experimenting with reinforcement learning.

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 dxyang/DQN_pytorch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
apache/mxnet
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. apache/mxnet · recommended 2×
  2. pytorch/pytorch · recommended 1×
  3. tensorflow/tensorflow · recommended 1×
  4. keras-team/keras · recommended 1×
  5. google/jax · recommended 1×
  • CATEGORY QUERY
    How can I implement deep Q-learning algorithms for Atari games using a modern deep learning framework?
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. TensorFlow (tensorflow/tensorflow)
    3. Keras (keras-team/keras)
    4. JAX (google/jax)
    5. Flax (google/flax)
    6. Haiku (deepmind/dm-haiku)
    7. MXNet (apache/mxnet)
    8. Gluon API (apache/mxnet)
    9. Gymnasium (Farama-Foundation/Gymnasium)

    AI recommended 9 alternatives but never named dxyang/DQN_pytorch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good PyTorch implementations for various deep Q-network architectures like dueling or double DQN?
    you: not recommended
    AI recommended (in order):
    1. Stable Baselines3
    2. PyTorch-RL
    3. OpenAI Baselines (PyTorch Port)
    4. RLlib
    5. CleanRL
    6. MinDQN

    AI recommended 6 alternatives but never named dxyang/DQN_pytorch. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 dxyang/DQN_pytorch?
    pass
    AI named dxyang/DQN_pytorch explicitly

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

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

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

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dxyang/DQN_pytorch — 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