RRepoGEO

REPOGEO REPORT · LITE

openai/maddpg

Default branch master · commit 3ceefa0a · scanned 5/13/2026, 1:57:47 AM

GitHub: 1,969 stars · 528 forks

AI VISIBILITY SCORE
74 /100
Needs work
Category recall
1 / 2
Avg rank #1.0 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 openai/maddpg, 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
  • hightopics#1
    Expand repository topics for better categorization

    Why:

    CURRENT
    paper
    COPY-PASTE FIX
    multi-agent-reinforcement-learning, deep-reinforcement-learning, maddpg, actor-critic, reinforcement-learning, multi-agent-systems
  • mediumreadme#2
    Add a 'Purpose and Scope' section to the README

    Why:

    COPY-PASTE FIX
    ## Purpose and Scope
This repository provides the original research implementation of the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. It is intended for researchers and practitioners interested in understanding, replicating, or building upon the MADDPG algorithm as presented in the associated paper. Given its archived status, it is not actively maintained or recommended for new production deployments, but serves as a valuable reference for multi-agent reinforcement learning studies.
  • lowreadme#3
    Add a small FAQ section to the README

    Why:

    COPY-PASTE FIX
    ## Frequently Asked Questions
*   **Is this repository actively maintained?** No, this codebase is archived and provided as-is. No further updates or active support are expected.
*   **Can I use this for new projects or production environments?** While the code is functional, it is primarily intended for research and historical reference. For new projects or production use, we recommend exploring more actively maintained multi-agent reinforcement learning frameworks and libraries.

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 openai/maddpg
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
9%
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. MAgent · recommended 1×
  5. Gym-MA · recommended 1×
  • CATEGORY QUERY
    How can I implement multi-agent reinforcement learning for mixed cooperative-competitive tasks?
    you: not recommended
    AI recommended (in order):
    1. PettingZoo
    2. RLlib
    3. OpenSpiel
    4. MAgent
    5. Gym-MA

    AI recommended 5 alternatives but never named openai/maddpg. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective deep reinforcement learning algorithms for training multiple interacting agents?
    you: #1
    AI recommended (in order):
    1. MADDPG ← you
    2. QMIX
    3. MAPPO
    4. COMA
    5. LIIR
    6. MA-POCA
    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 openai/maddpg?
    pass
    AI named openai/maddpg explicitly

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

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

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

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MARKDOWN (README)
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openai/maddpg — 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