RRepoGEO

REPOGEO REPORT · LITE

DeepRLChinese/DeepRL-Chinese

Default branch master · commit 55a9f5e9 · scanned 6/8/2026, 1:32:32 AM

GitHub: 869 stars · 144 forks

AI VISIBILITY SCORE
17 /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
1 / 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 DeepRLChinese/DeepRL-Chinese, 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
  • highabout#1
    Add a clear "About" description for the repository

    Why:

    COPY-PASTE FIX
    Python implementations of core Deep Reinforcement Learning (DRL) algorithms, designed for learning and educational purposes. Includes examples for DQN, SARSA, Dueling DQN, and Double DQN on environments like CartPole and Pendulum.
  • mediumlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0, or GPL-3.0) that best suits the project's intent.

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 DeepRLChinese/DeepRL-Chinese
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DLR-RM/stable-baselines3
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. DLR-RM/stable-baselines3 · recommended 1×
  2. ray-project/ray · recommended 1×
  3. vwxyzjn/cleanrl · recommended 1×
  4. tensorflow/agents · recommended 1×
  5. keras-rl/keras-rl2 · recommended 1×
  • CATEGORY QUERY
    What are some Python implementations for learning deep reinforcement learning algorithms?
    you: not recommended
    AI recommended (in order):
    1. Stable Baselines3 (DLR-RM/stable-baselines3)
    2. RLlib (ray-project/ray)
    3. CleanRL (vwxyzjn/cleanrl)
    4. TensorFlow Agents (tensorflow/agents)
    5. Keras-RL2 (keras-rl/keras-rl2)
    6. DeepMind's Acme (deepmind/acme)

    AI recommended 6 alternatives but never named DeepRLChinese/DeepRL-Chinese. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I find Python examples for classic deep reinforcement learning algorithms like DQN?
    you: not recommended
    AI recommended (in order):
    1. Stable Baselines3
    2. PyTorch Reinforcement Learning Examples
    3. TensorFlow Agents
    4. Keras-RL2
    5. CleanRL
    6. Deep Reinforcement Learning Hands-On

    AI recommended 6 alternatives but never named DeepRLChinese/DeepRL-Chinese. 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 DeepRLChinese/DeepRL-Chinese?
    pass
    AI did not name DeepRLChinese/DeepRL-Chinese — 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 DeepRLChinese/DeepRL-Chinese in production, what risks or prerequisites should they evaluate first?
    pass
    AI named DeepRLChinese/DeepRL-Chinese 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 DeepRLChinese/DeepRL-Chinese solve, and who is the primary audience?
    pass
    AI did not name DeepRLChinese/DeepRL-Chinese — 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

Drop this badge into the README of DeepRLChinese/DeepRL-Chinese. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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DeepRLChinese/DeepRL-Chinese — 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