RRepoGEO

REPOGEO REPORT · LITE

Replicable-MARL/MARLlib

Default branch master · commit 80e9973a · scanned 5/11/2026, 11:16:47 AM

GitHub: 1,312 stars · 194 forks

AI VISIBILITY SCORE
68 /100
Needs work
Category recall
1 / 2
Avg rank #3.0 when recommended
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 Replicable-MARL/MARLlib, 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
  • highreadme#1
    Reposition and strengthen the README's opening statement

    Why:

    CURRENT
    The core definition of MARLlib appears after badges and a news section in the README.
    COPY-PASTE FIX
    Immediately after the H1, add: 'MARLlib is the comprehensive Multi-agent Reinforcement Learning (MARL) library built on Ray and RLlib, offering a unified platform for developing, training, and testing MARL algorithms across various tasks. It is designed to be the single repository necessary for all your MARL research and development needs.'
  • mediumabout#2
    Enhance the 'About' description to emphasize comprehensiveness

    Why:

    CURRENT
    One repository is all that is necessary for Multi-agent Reinforcement Learning (MARL)
    COPY-PASTE FIX
    MARLlib is the comprehensive, unified, and efficient library for Multi-agent Reinforcement Learning (MARL), built on Ray and RLlib, making it the only repository you need for developing, training, and testing MARL algorithms.
  • lowtopics#3
    Add a 'marl-framework' topic

    Why:

    CURRENT
    deep-reinforcement-learning, multi-agent-reinforcement-learning, pytorch, ray, rllib
    COPY-PASTE FIX
    deep-reinforcement-learning, multi-agent-reinforcement-learning, pytorch, ray, rllib, marl-framework

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
1 / 2
50% of queries surface Replicable-MARL/MARLlib
Avg rank
#3.0
Lower is better. #1 = top recommendation.
Share of voice
10%
Of all named tools, what % are you?
Top rival
PettingZoo
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PettingZoo · recommended 1×
  2. RLlib · recommended 1×
  3. OpenSpiel · recommended 1×
  4. MARL-Algorithms · recommended 1×
  5. MAgent · recommended 1×
  • CATEGORY QUERY
    Looking for a comprehensive library to implement multi-agent reinforcement learning algorithms efficiently.
    you: not recommended
    AI recommended (in order):
    1. PettingZoo
    2. RLlib
    3. OpenSpiel
    4. MARL-Algorithms
    5. MAgent

    AI recommended 5 alternatives but never named Replicable-MARL/MARLlib. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good multi-agent deep reinforcement learning frameworks compatible with PyTorch and Ray?
    you: #3
    AI recommended (in order):
    1. RLlib (ray-project/ray)
    2. PettingZoo (Farama-Foundation/PettingZoo)
    3. MARLlib (marl-lib/marl-lib) ← you
    4. OpenSpiel (deepmind/open_spiel)
    5. TorchRL (pytorch/rl)
    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 Replicable-MARL/MARLlib?
    pass
    AI named Replicable-MARL/MARLlib explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts Replicable-MARL/MARLlib in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Replicable-MARL/MARLlib 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 Replicable-MARL/MARLlib solve, and who is the primary audience?
    pass
    AI named Replicable-MARL/MARLlib explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of Replicable-MARL/MARLlib. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/Replicable-MARL/MARLlib.svg)](https://repogeo.com/en/r/Replicable-MARL/MARLlib)
HTML
<a href="https://repogeo.com/en/r/Replicable-MARL/MARLlib"><img src="https://repogeo.com/badge/Replicable-MARL/MARLlib.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

Replicable-MARL/MARLlib — 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