RRepoGEO

REPOGEO REPORT · LITE

0russwest0/Awesome-Agent-RL

Default branch main · commit b2ed27ee · scanned 6/3/2026, 10:53:04 PM

GitHub: 512 stars · 21 forks

AI VISIBILITY SCORE
23 /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
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 0russwest0/Awesome-Agent-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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise description to the repository's About section

    Why:

    COPY-PASTE FIX
    A curated collection of papers and resources focused on applying Reinforcement Learning to develop intelligent agents, particularly for multi-turn LLM agents.
  • mediumreadme#2
    Enhance the README's opening tagline to explicitly state its 'awesome list' nature and target audience

    Why:

    CURRENT
    **Curated collection of papers and resources on unlocking the potential of Agents through Reinforcement Learning**
    COPY-PASTE FIX
    **A curated collection of essential papers and resources for researchers and practitioners exploring Reinforcement Learning to develop intelligent agents, especially for multi-turn LLM agents.**

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 0russwest0/Awesome-Agent-RL
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI Gym / Gymnasium
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI Gym / Gymnasium · recommended 1×
  2. Stable Baselines3 (SB3) · recommended 1×
  3. Ray RLlib · recommended 1×
  4. DeepMind's Acme · recommended 1×
  5. TensorFlow Agents (TF-Agents) · recommended 1×
  • CATEGORY QUERY
    What are the best resources for applying reinforcement learning to develop intelligent agents?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym / Gymnasium
    2. Stable Baselines3 (SB3)
    3. Ray RLlib
    4. DeepMind's Acme
    5. TensorFlow Agents (TF-Agents)
    6. PyTorch-Lightning-RL
    7. Unity ML-Agents

    AI recommended 7 alternatives but never named 0russwest0/Awesome-Agent-RL. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for research and tools on multi-turn reinforcement learning for LLM agents.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face's TRL (huggingface/trl)
    2. DeepMind's Acme (deepmind/acme)
    3. OpenAI's Baselines (openai/baselines)
    4. LangChain (langchain-ai/langchain)
    5. LlamaIndex (run-llama/llama_index)
    6. OpenAI API
    7. Hugging Face Transformers (huggingface/transformers)
    8. ParlAI (facebookresearch/ParlAI)

    AI recommended 8 alternatives but never named 0russwest0/Awesome-Agent-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
    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 0russwest0/Awesome-Agent-RL?
    pass
    AI named 0russwest0/Awesome-Agent-RL explicitly

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

  • If a team adopts 0russwest0/Awesome-Agent-RL in production, what risks or prerequisites should they evaluate first?
    pass
    AI named 0russwest0/Awesome-Agent-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 0russwest0/Awesome-Agent-RL solve, and who is the primary audience?
    pass
    AI did not name 0russwest0/Awesome-Agent-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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0russwest0/Awesome-Agent-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