RRepoGEO

REPOGEO REPORT · LITE

PKU-MARL/Multi-Agent-Transformer

Default branch main · commit be3ff49c · scanned 6/11/2026, 10:43:03 PM

GitHub: 509 stars · 92 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 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 PKU-MARL/Multi-Agent-Transformer, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Strengthen README's opening sentence to clearly state its category

    Why:

    CURRENT
    This is the **official implementation** of MAT. MAT is a novel neural network based on the encoder-decoder architecture that implements a multi-agent learning process through sequence models, aiming to build the bridge between MARL and SM so that the modeling power of modern sequence models, the Transformer, can be unleashed for MARL.
    COPY-PASTE FIX
    This repository provides the **official implementation** of Multi-Agent Transformer (MAT), a novel framework for **Multi-Agent Reinforcement Learning (MARL)** that leverages **Transformer models** to bridge MARL and sequence modeling. MAT is an encoder-decoder architecture designed for cooperative MARL tasks.
  • highlicense#2
    Add a LICENSE file to clarify usage terms

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0) to clearly state the terms of use.

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 PKU-MARL/Multi-Agent-Transformer
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. pytorch/pytorch · recommended 1×
  3. ray-project/ray · recommended 1×
  4. oxwhirl/pymarl · recommended 1×
  5. uoe-agents/epymarl · recommended 1×
  • CATEGORY QUERY
    How to apply transformer models for cooperative multi-agent reinforcement learning tasks?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PyTorch (pytorch/pytorch)
    3. RLlib (ray-project/ray)
    4. PyMARL (oxwhirl/pymarl)
    5. EPymarl (uoe-agents/epymarl)
    6. JAX (google/jax)
    7. Flax (google/flax)
    8. Gymnasium (Farama-Foundation/Gymnasium)
    9. OpenAI Gym (openai/gym)

    AI recommended 9 alternatives but never named PKU-MARL/Multi-Agent-Transformer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective online deep reinforcement learning frameworks for multi-agent environments?
    you: not recommended
    AI recommended (in order):
    1. RLlib
    2. PettingZoo
    3. OpenSpiel
    4. MARL-Baselines
    5. PyMARL
    6. Stable Baselines3

    AI recommended 6 alternatives but never named PKU-MARL/Multi-Agent-Transformer. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 PKU-MARL/Multi-Agent-Transformer?
    pass
    AI named PKU-MARL/Multi-Agent-Transformer explicitly

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

  • If a team adopts PKU-MARL/Multi-Agent-Transformer in production, what risks or prerequisites should they evaluate first?
    pass
    AI named PKU-MARL/Multi-Agent-Transformer 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 PKU-MARL/Multi-Agent-Transformer solve, and who is the primary audience?
    pass
    AI did not name PKU-MARL/Multi-Agent-Transformer — 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?

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PKU-MARL/Multi-Agent-Transformer — 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