RRepoGEO

REPOGEO REPORT · LITE

Replicable-MARL/MARLlib

Default branch master · commit 80e9973a · scanned 6/21/2026, 3:51:43 PM

GitHub: 1,333 stars · 198 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
71 /100
Needs work
Category recall
1 / 2
Avg rank #2.0 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
  • highabout#1
    Refine the repository description to highlight its research and development focus

    Why:

    CURRENT
    One repository is all that is necessary for Multi-agent Reinforcement Learning (MARL)
    COPY-PASTE FIX
    A comprehensive, scalable, and replicable framework for developing, evaluating, and benchmarking multi-agent reinforcement learning (MARL) algorithms for researchers and developers.
  • mediumtopics#2
    Add more specific topics to improve category visibility

    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, marl-benchmarking, reinforcement-learning-research, multi-agent-systems
  • lowreadme#3
    Strengthen the README's opening paragraph to emphasize its comprehensive research utility

    Why:

    CURRENT
    Multi-agent Reinforcement Learning Library (MARLlib) is a MARL librarythat utilizes **Ray** and one of its toolkits **RLlib**. It offers a comprehensive platform for developing, training, and testing MARL algorithms across various tasks
    COPY-PASTE FIX
    MARLlib is a comprehensive Multi-agent Reinforcement Learning (MARL) library built on **Ray** and **RLlib**, providing a scalable and replicable platform for researchers and developers to develop, train, evaluate, and benchmark a wide range of MARL algorithms across diverse tasks.

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
#2.0
Lower is better. #1 = top recommendation.
Share of voice
8%
Of all named tools, what % are you?
Top rival
PettingZoo
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PettingZoo · recommended 2×
  2. RLlib · recommended 2×
  3. Stable Baselines3 · recommended 2×
  4. OpenSpiel · recommended 1×
  5. MARL-Algorithms · recommended 1×
  • CATEGORY QUERY
    What's a good library for developing and researching multi-agent reinforcement learning algorithms?
    you: not recommended
    AI recommended (in order):
    1. PettingZoo
    2. RLlib
    3. OpenSpiel
    4. MARL-Algorithms
    5. Stable Baselines3
    6. MAgent

    AI recommended 6 alternatives but never named Replicable-MARL/MARLlib. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a comprehensive deep learning framework for multi-agent reinforcement learning experiments using PyTorch.
    you: #2
    AI recommended (in order):
    1. RLlib
    2. MARLlib ← you
    3. PettingZoo
    4. PyMARL
    5. TorchRL
    6. Stable Baselines3
    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?

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  • Brand-free category queries5 vs 2 in Lite
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