RRepoGEO

REPOGEO REPORT · LITE

walkinglabs/hands-on-modern-rl

Default branch main · commit a727c163 · scanned 6/9/2026, 12:47:46 PM

GitHub: 2,804 stars · 169 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)

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

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 walkinglabs/hands-on-modern-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
    Reposition the README's opening statement to explicitly emphasize "curriculum" or "course"

    Why:

    CURRENT
    <p><em>A practice-first guide to modern RL, from classic control to LLM post-training, RLVR, and multimodal agents.</em></p>
    COPY-PASTE FIX
    <p><em>A comprehensive, practice-first curriculum and course for modern Reinforcement Learning, from classic control to LLM post-training, RLVR, and multimodal agents.</em></p>
  • mediumreadme#2
    Add a concise value proposition immediately after the main title in the README

    Why:

    COPY-PASTE FIX
    This repository provides a structured learning path with line-by-line code explanations, bridging theoretical foundations to cutting-edge applications like LLM alignment and advanced agentic systems, making complex topics accessible for practical implementation.
  • lowreadme#3
    Explicitly state the project's license(s) in the README

    Why:

    COPY-PASTE FIX
    This project is licensed under [Specify License(s) here, e.g., 'the Apache-2.0 License and MIT License for specific components']. See the [LICENSE](LICENSE) file for full details.

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 walkinglabs/hands-on-modern-rl
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/trl
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/trl · recommended 1×
  2. DeepMind · recommended 1×
  3. OpenAI's InstructGPT · recommended 1×
  4. Carnegie Mellon University · recommended 1×
  5. Stanford's CS224N · recommended 1×
  • CATEGORY QUERY
    Where can I find a hands-on guide to modern reinforcement learning for LLM alignment?
    you: not recommended
    AI recommended (in order):
    1. TRL Library (huggingface/trl)
    2. DeepMind
    3. OpenAI's InstructGPT
    4. Carnegie Mellon University
    5. Stanford's CS224N
    6. Stanford's CS234
    7. Reinforcement Learning: An Introduction by Sutton and Barto

    AI recommended 7 alternatives but never named walkinglabs/hands-on-modern-rl. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a practical curriculum to learn agentic AI systems from foundational RL concepts.
    you: not recommended
    AI recommended (in order):
    1. Reinforcement Learning: An Introduction
    2. Coursera's Reinforcement Learning Specialization by the University of Alberta
    3. Gymnasium (Farama-Foundation/Gymnasium)
    4. Stable Baselines3 (DLR-RM/stable-baselines3)
    5. PyTorch (pytorch/pytorch)
    6. TensorFlow (tensorflow/tensorflow)
    7. Deep Reinforcement Learning by UC Berkeley (CS285)
    8. Ray RLlib (ray-project/ray)
    9. Multi-Agent Reinforcement Learning: A Survey
    10. PettingZoo (Farama-Foundation/PettingZoo)
    11. MADDPG
    12. QMIX
    13. SOAR
    14. ACT-R
    15. Feudal Reinforcement Learning
    16. Option-Critic Architectures
    17. OpenAI API (GPT-4, GPT-3.5 Turbo)
    18. Anthropic Claude API
    19. LangChain (langchain-ai/langchain)
    20. LlamaIndex (run-llama/llama_index)
    21. AutoGPT (Significant-Gravitas/AutoGPT)
    22. BabyAGI (yoheinakajima/babyagi)
    23. Google Search API
    24. Pinecone
    25. ChromaDB (chroma-core/chroma)

    AI recommended 25 alternatives but never named walkinglabs/hands-on-modern-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
    pass

  • 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 walkinglabs/hands-on-modern-rl?
    pass
    AI did not name walkinglabs/hands-on-modern-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 walkinglabs/hands-on-modern-rl in production, what risks or prerequisites should they evaluate first?
    pass
    AI named walkinglabs/hands-on-modern-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 walkinglabs/hands-on-modern-rl solve, and who is the primary audience?
    pass
    AI did not name walkinglabs/hands-on-modern-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?

Embed your GEO score

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

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/walkinglabs/hands-on-modern-rl.svg)](https://repogeo.com/en/r/walkinglabs/hands-on-modern-rl)
HTML
<a href="https://repogeo.com/en/r/walkinglabs/hands-on-modern-rl"><img src="https://repogeo.com/badge/walkinglabs/hands-on-modern-rl.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

walkinglabs/hands-on-modern-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
walkinglabs/hands-on-modern-rl — RepoGEO report