REPOGEO REPORT · LITE

aikorea/awesome-rl

Default branch master · commit 774cb664 · scanned 6/26/2026, 5:57:48 AM

GitHub: 9,832 stars · 1,934 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

35 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 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 aikorea/awesome-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

Remove or clarify the 'no longer maintained' statement in the README

Why:

CURRENT

This page is no longer maintained.

COPY-PASTE FIX

(Remove this line, or replace with a statement clarifying its current status if it's still a valuable, albeit archived, resource.)

hightopics#2

Add relevant topics to the repository

Why:

COPY-PASTE FIX

reinforcement-learning, awesome-list, machine-learning, deep-learning, ai, education, resources

highlicense#3
Add a LICENSE file to the repository
Why:
COPY-PASTE FIX
```
Add a LICENSE file (e.g., MIT License) to the root of the repository.
```

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 aikorea/awesome-rl

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Reinforcement Learning: An Introduction" by Sutton and Barto

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Reinforcement Learning: An Introduction" by Sutton and Barto · recommended 1×
Coursera's "Reinforcement Learning Specialization" by the University of Alberta · recommended 1×
DeepMind's "Introduction to Reinforcement Learning" course (David Silver's lectures) · recommended 1×
"Hands-On Reinforcement Learning with Python" by Sudharsan Ravichandiran · recommended 1×
TensorFlow · recommended 1×

CATEGORY QUERY
Where can I find comprehensive resources to learn reinforcement learning theory and applications?
you: not recommended
AI recommended (in order):
1. Reinforcement Learning: An Introduction" by Sutton and Barto
2. Coursera's "Reinforcement Learning Specialization" by the University of Alberta
3. DeepMind's "Introduction to Reinforcement Learning" course (David Silver's lectures)
4. "Hands-On Reinforcement Learning with Python" by Sudharsan Ravichandiran
5. TensorFlow
6. PyTorch
7. "Reinforcement Learning: State-of-the-Art"
8. OpenAI Spinning Up in Deep RL
9. "Deep Reinforcement Learning Hands-On" by Maxim Lapan
AI recommended 9 alternatives but never named aikorea/awesome-rl. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are some good open-source platforms and code examples for reinforcement learning projects?
you: not recommended
AI recommended (in order):
1. Stable Baselines3 (DLR-RM/stable-baselines3)
2. RLlib (ray-project/ray)
3. CleanRL (vwxyzjn/cleanrl)
4. Tianshou (thu-ml/tianshou)
5. Keras-RL (keras-rl/keras-rl)
6. Minigrid (Farama-Foundation/Minigrid)
7. Gymnasium (Farama-Foundation/Gymnasium)
AI recommended 7 alternatives but never named aikorea/awesome-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 aikorea/awesome-rl?
pass
AI named aikorea/awesome-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 aikorea/awesome-rl in production, what risks or prerequisites should they evaluate first?
pass
AI named aikorea/awesome-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 aikorea/awesome-rl solve, and who is the primary audience?
pass
AI named aikorea/awesome-rl explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/aikorea/awesome-rl.svg)](https://repogeo.com/en/r/aikorea/awesome-rl)

HTML

<a href="https://repogeo.com/en/r/aikorea/awesome-rl"><img src="https://repogeo.com/badge/aikorea/awesome-rl.svg" alt="RepoGEO" /></a>

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aikorea/awesome-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