REPOGEO REPORT · LITE
yingchengyang/Reinforcement-Learning-Papers
Default branch main · commit eac64e1e · scanned 6/1/2026, 11:37:41 AM
GitHub: 571 stars · 42 forks
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 yingchengyang/Reinforcement-Learning-Papers, 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.
- highreadme#1Reposition the README's opening sentence to clarify the repo's nature
Why:
CURRENTRelated papers for Reinforcement Learning (we mainly focus on single-agent).
COPY-PASTE FIXThis GitHub repository provides a curated collection of insightful research papers on Reinforcement Learning, primarily focusing on single-agent methods.
- mediumreadme#2Add a sentence highlighting the unique inline summaries feature
Why:
COPY-PASTE FIXEach paper entry includes a direct link to the source and a brief, inline summary to quickly grasp its core contribution.
- lowhomepage#3Add the repository URL as the homepage
Why:
COPY-PASTE FIXhttps://github.com/yingchengyang/Reinforcement-Learning-Papers
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.
- Papers With Code · recommended 1×
- arXiv Sanity Preserver · recommended 1×
- RL Theory · recommended 1×
- The Batch · recommended 1×
- Twitter · recommended 1×
- CATEGORY QUERYWhere can I find a curated list of recent reinforcement learning research papers?you: not recommendedAI recommended (in order):
- Papers With Code
- arXiv Sanity Preserver
- RL Theory
- The Batch
AI recommended 6 alternatives but never named yingchengyang/Reinforcement-Learning-Papers. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat are the foundational and current research trends in model-based reinforcement learning?you: not recommendedAI recommended (in order):
- Gaussian Processes
- Bayesian Neural Networks
- Neural Networks
- MLPs
- LSTMs
- Transformers
- Model Predictive Control
- Random Shooting
- Cross-Entropy Method
- Model Predictive Path Integral
- Monte Carlo Tree Search
- AlphaGo
- Variational Inference
- Monte Carlo Dropout
- Dyna-style algorithms
- Model-Based Value Expansion (MVE)
- World Models
- DreamerV3
- Model-Based Policy Optimization (MBPO)
- Foundation Models
AI recommended 20 alternatives but never named yingchengyang/Reinforcement-Learning-Papers. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
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 yingchengyang/Reinforcement-Learning-Papers?passAI named yingchengyang/Reinforcement-Learning-Papers explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts yingchengyang/Reinforcement-Learning-Papers in production, what risks or prerequisites should they evaluate first?passAI did not name yingchengyang/Reinforcement-Learning-Papers — 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?
- In one sentence, what problem does the repo yingchengyang/Reinforcement-Learning-Papers solve, and who is the primary audience?passAI did not name yingchengyang/Reinforcement-Learning-Papers — 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
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yingchengyang/Reinforcement-Learning-Papers — 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