REPOGEO REPORT · LITE

LantaoYu/MARL-Papers

Default branch master · commit ea0df368 · scanned 5/17/2026, 6:17:48 AM

GitHub: 4,823 stars · 775 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)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

22 /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

1 / 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 LantaoYu/MARL-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.

OVERALL DIRECTION

highlicense#1
Add a LICENSE file to the repository
Why:
COPY-PASTE FIX
```
Create a LICENSE file (e.g., MIT, Apache-2.0, or GPL-3.0) in the repository root.
```

highreadme#2

Strengthen the README's opening paragraph to emphasize unique value

Why:

CURRENT

This is a collection of research and review papers of multi-agent reinforcement learning (MARL). The Papers are sorted by time. Any suggestions and pull requests are welcome.

COPY-PASTE FIX

This repository provides a **curated and actively maintained collection of research and review papers** in Multi-Agent Reinforcement Learning (MARL), meticulously organized by time and topic. Unlike generic search engines or broad academic databases, this list offers a focused and structured resource for researchers, academics, and students to explore foundational concepts and the latest advancements in MARL.

mediumtopics#3

Expand repository topics for better categorization

Why:

CURRENT

multi-agent-learning, multiagent-reinforcement-learning

COPY-PASTE FIX

multi-agent-learning, multiagent-reinforcement-learning, paper-list, awesome-list, research-papers, literature-review, marl-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.

Recall

0 / 2

0% of queries surface LantaoYu/MARL-Papers

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Awesome-MARL

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Awesome-MARL · recommended 1×
Google Scholar · recommended 1×
arXiv.org · recommended 1×
Papers With Code · recommended 1×
NeurIPS · recommended 1×

CATEGORY QUERY
Where can I find a comprehensive list of research papers on multi-agent reinforcement learning?
you: not recommended
AI recommended (in order):
1. Awesome-MARL
2. Google Scholar
3. arXiv.org
4. Papers With Code
5. NeurIPS
6. ICML
7. ICLR
8. AAMAS
9. AAAI
10. OpenReview
11. PMLR
AI recommended 11 alternatives but never named LantaoYu/MARL-Papers. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the latest advancements and foundational concepts in multi-agent reinforcement learning?
you: not recommended
AI recommended (in order):
1. RLlib (Ray) (ray-project/ray)
2. PettingZoo (Farama-Foundation/PettingZoo)
3. PyMARL (with SMAC) (oxwhirl/pymarl)
4. OpenSpiel (deepmind/open_spiel)
5. TorchMARL (hijkzzz/TorchMARL)
6. MAgent (PKU-MARL/MAgent)
7. MARL-Baselines (CleanRL) (vwxyzjn/cleanrl)
AI recommended 7 alternatives but never named LantaoYu/MARL-Papers. 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 LantaoYu/MARL-Papers?
pass
AI did not name LantaoYu/MARL-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 LantaoYu/MARL-Papers in production, what risks or prerequisites should they evaluate first?
pass
AI named LantaoYu/MARL-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 LantaoYu/MARL-Papers solve, and who is the primary audience?
pass
AI did not name LantaoYu/MARL-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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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/LantaoYu/MARL-Papers"><img src="https://repogeo.com/badge/LantaoYu/MARL-Papers.svg" alt="RepoGEO" /></a>

Pro

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LantaoYu/MARL-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