RRepoGEO

REPOGEO REPORT · LITE

tigerneil/awesome-deep-rl

Default branch master · commit ccfc8116 · scanned 6/24/2026, 6:16:54 PM

GitHub: 1,509 stars · 223 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
55 /100
Needs work
Category recall
1 / 2
Avg rank #1.0 when recommended
Rule findings
1 pass · 1 warn · 0 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 tigerneil/awesome-deep-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
    Explicitly state the breadth of DRL sub-fields covered in the README introduction

    Why:

    CURRENT
    Reinforcement learning is the fundamental framework for building AGI. Therefore we share important contributions within this awesome drl project.
    COPY-PASTE FIX
    This awesome list curates important contributions across Deep Reinforcement Learning, including specialized areas like unsupervised, offline, multi-agent, and hierarchical RL, serving as a comprehensive resource.
  • mediumabout#2
    Refine the repository description to clearly state its purpose as a curated resource

    Why:

    CURRENT
    For deep RL and the future of AI.
    COPY-PASTE FIX
    A comprehensive, curated collection of resources and papers in Deep Reinforcement Learning, including specialized areas like unsupervised, offline, and multi-agent RL.
  • lowhomepage#3
    Add a relevant homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add a URL to a project website, blog, or related resource if available.

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
1 / 2
50% of queries surface tigerneil/awesome-deep-rl
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
Papers With Code
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Papers With Code · recommended 1×
  2. OpenAI Spinning Up in Deep RL · recommended 1×
  3. RLlib · recommended 1×
  4. DeepMind's Publications Page · recommended 1×
  5. ArXiv · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive collection of resources for deep reinforcement learning research?
    you: #1
    AI recommended (in order):
    1. Awesome-Deep-RL ← you
    2. Papers With Code
    3. OpenAI Spinning Up in Deep RL
    4. RLlib
    5. DeepMind's Publications Page
    6. ArXiv
    7. Medium
    Show full AI answer
  • CATEGORY QUERY
    What are the best tools for exploring unsupervised or offline reinforcement learning techniques?
    you: not recommended
    AI recommended (in order):
    1. RLlib (Ray) (ray-project/ray)
    2. Stable Baselines3 (SB3) (DLR-RM/stable-baselines3)
    3. D4RL (Datasets for Deep Data-Driven Reinforcement Learning) (rail-berkeley/d4rl)
    4. Acme (DeepMind) (deepmind/acme)
    5. Tianshou (thu-ml/tianshou)
    6. OpenAI Gym/Farama Gymnasium (Farama-Foundation/Gymnasium)

    AI recommended 6 alternatives but never named tigerneil/awesome-deep-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 tigerneil/awesome-deep-rl?
    pass
    AI did not name tigerneil/awesome-deep-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?

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