REPOGEO REPORT · LITE
tigerneil/awesome-deep-rl
Default branch master · commit ccfc8116 · scanned 5/14/2026, 6:02:41 AM
GitHub: 1,512 stars · 223 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 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.
- highreadme#1Reposition README introduction to clearly state it's a curated list of resources
Why:
CURRENTReinforcement learning is the fundamental framework for building AGI. Therefore we share important contributions within this awesome drl project.
COPY-PASTE FIXThis project is a curated, annotated list of important research papers, foundational works, and key resources in Deep Reinforcement Learning, designed to help researchers and practitioners navigate the field.
- mediumhomepage#2Add a homepage URL to the repository's 'About' section
Why:
COPY-PASTE FIXhttps://github.com/tigerneil/awesome-deep-rl
- mediumreadme#3Explicitly highlight the unique value proposition of concise summaries in the README
Why:
COPY-PASTE FIXEach entry includes concise summaries and key takeaways, going beyond typical link-only lists to provide an annotated guide.
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.
- DeepMind Publications · recommended 1×
- OpenAI Blog and Research · recommended 1×
- Papers With Code · recommended 1×
- arXiv · recommended 1×
- RL Theory and Algorithms (RLTA) Workshop at NeurIPS/ICML · recommended 1×
- CATEGORY QUERYWhere can I find a comprehensive overview of recent advancements in deep reinforcement learning?you: not recommendedAI recommended (in order):
- DeepMind Publications
- OpenAI Blog and Research
- Papers With Code
- arXiv
- RL Theory and Algorithms (RLTA) Workshop at NeurIPS/ICML
- "Reinforcement Learning: An Introduction" by Sutton and Barto
- The Batch by The Gradient
AI recommended 7 alternatives but never named tigerneil/awesome-deep-rl. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat are the best resources for understanding hierarchical or multi-agent reinforcement learning techniques?you: not recommendedAI recommended (in order):
- OpenAI Baselines
- Stable Baselines3
- RLlib
AI recommended 3 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 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 tigerneil/awesome-deep-rl?passAI 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?passAI 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?passAI 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?
Embed your GEO score
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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