REPOGEO REPORT · LITE
hanjuku-kaso/awesome-offline-rl
Default branch main · commit e89fcd8f · scanned 5/27/2026, 4:37:55 AM
GitHub: 1,064 stars · 93 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 hanjuku-kaso/awesome-offline-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.
- highlicense#1Add a LICENSE file to the repository
Why:
COPY-PASTE FIXCreate a `LICENSE` file in the repository root, for example, by adding the text of the MIT License or Apache-2.0 License to clarify how others can use and contribute to this project.
- highreadme#2Strengthen README's positioning as a definitive resource
Why:
CURRENTThis is a collection of research and review papers for **offline reinforcement learning (offline rl)**.
COPY-PASTE FIXThis is the **definitive and comprehensive collection** of research and review papers for **offline reinforcement learning (offline rl)**, designed to be the go-to index for algorithms and methods.
- mediumhomepage#3Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIXAdd a relevant homepage URL (e.g., a project website, a related research group page, or a link to the main paper if applicable) in the repository settings.
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.
- Awesome Offline Reinforcement Learning GitHub Repository · recommended 1×
- RL Unplugged · recommended 1×
- D4RL · recommended 1×
- Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems (Levine et al. 2020) · recommended 1×
- Google Scholar · recommended 1×
- CATEGORY QUERYWhere can I find a comprehensive list of algorithms for offline reinforcement learning?you: not recommendedAI recommended (in order):
- Awesome Offline Reinforcement Learning GitHub Repository
- RL Unplugged
- D4RL
- Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems (Levine et al. 2020)
- Google Scholar
- arXiv
- CORL (Conservative Offline Reinforcement Learning)
- d3rlpy
- NeurIPS
- ICML
- ICLR
AI recommended 11 alternatives but never named hanjuku-kaso/awesome-offline-rl. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhat are the latest research papers and methods in off-policy evaluation for RL?you: not recommendedAI recommended (in order):
- Doubly Robust
- Doubly Robust Off-Policy Evaluation with Shrinkage
- Doubly Robust Off-Policy Evaluation with Inverse Propensity Score Weighting
- Magic Policy Optimization
- Model-Based Off-Policy Evaluation
- Self-Normalized Importance Sampling
- Per-Decision Importance Sampling
- Minimax OPE
- Pessimistic OPE
- Gradient-Based OPE
- Policy Gradient-based OPE
- Contextual Bandits OPE
- Causal Inference-based OPE
AI recommended 13 alternatives but never named hanjuku-kaso/awesome-offline-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 hanjuku-kaso/awesome-offline-rl?passAI did not name hanjuku-kaso/awesome-offline-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 hanjuku-kaso/awesome-offline-rl in production, what risks or prerequisites should they evaluate first?passAI named hanjuku-kaso/awesome-offline-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 hanjuku-kaso/awesome-offline-rl solve, and who is the primary audience?passAI did not name hanjuku-kaso/awesome-offline-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
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hanjuku-kaso/awesome-offline-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