REPOGEO REPORT · LITE
benedekrozemberczki/awesome-monte-carlo-tree-search-papers
Default branch master · commit de0d854f · scanned 6/3/2026, 5:11:51 PM
GitHub: 704 stars · 74 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 benedekrozemberczki/awesome-monte-carlo-tree-search-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#1Clarify repo's nature as an 'awesome list' in the README's opening.
Why:
CURRENT# Awesome Monte Carlo Tree Search Papers.
COPY-PASTE FIX# Awesome Monte Carlo Tree Search Papers. This repository is a curated **awesome list** of academic papers on Monte Carlo Tree Search, including links to their implementations. It serves as a focused resource for researchers and practitioners, distinct from general search engines or software libraries.
- mediumhomepage#2Add the repository URL as the homepage.
Why:
COPY-PASTE FIXhttps://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers
- lowtopics#3Add topics that describe the repository's format.
Why:
CURRENTatari, deep-learning, deep-q-learning, learning, machine-learning, machine-learning-algorithms, monte-carlo, monte-carlo-tree-search, policy-evaluation, policy-gradient, q-learning, reinforcement-learning, reinforcement-learning-agent, reinforcement-learning-algorithms, rl, tree-search
COPY-PASTE FIXawesome-list, paper-list, curated-list, atari, deep-learning, deep-q-learning, learning, machine-learning, machine-learning-algorithms, monte-carlo, monte-carlo-tree-search, policy-evaluation, policy-gradient, q-learning, reinforcement-learning, reinforcement-learning-agent, reinforcement-learning-algorithms, rl, tree-search
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.
- Google Scholar · recommended 1×
- arXiv · recommended 1×
- GitHub · recommended 1×
- OpenAI · recommended 1×
- DeepMind · recommended 1×
- CATEGORY QUERYWhere can I find research papers and code for Monte Carlo Tree Search algorithms?you: not recommendedAI recommended (in order):
- Google Scholar
- arXiv
- GitHub
- OpenAI
- DeepMind
- AlphaZero-General (applied-ai-lab/AlphaZero-General)
- Leela Chess Zero (LC0)
- KataGo
- ResearchGate
- Stanford
- MIT
- CMU
- Medium
- Towards Data Science
AI recommended 14 alternatives but never named benedekrozemberczki/awesome-monte-carlo-tree-search-papers. This is the gap to close.
Show full AI answer
- CATEGORY QUERYSeeking practical implementations of advanced tree search methods for reinforcement learning problems.you: not recommendedAI recommended (in order):
- Leela Chess Zero
- OpenSpiel
- MCTS.py
- RLlib
- Minigo
- Gym-MCTS
- Acme
AI recommended 7 alternatives but never named benedekrozemberczki/awesome-monte-carlo-tree-search-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 benedekrozemberczki/awesome-monte-carlo-tree-search-papers?passAI did not name benedekrozemberczki/awesome-monte-carlo-tree-search-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?
- If a team adopts benedekrozemberczki/awesome-monte-carlo-tree-search-papers in production, what risks or prerequisites should they evaluate first?passAI named benedekrozemberczki/awesome-monte-carlo-tree-search-papers 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 benedekrozemberczki/awesome-monte-carlo-tree-search-papers solve, and who is the primary audience?passAI did not name benedekrozemberczki/awesome-monte-carlo-tree-search-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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benedekrozemberczki/awesome-monte-carlo-tree-search-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