RRepoGEO

REPOGEO REPORT · LITE

datamllab/awesome-game-ai

Default branch master · commit 85c09c04 · scanned 6/14/2026, 2:23:12 PM

GitHub: 968 stars · 117 forks

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 datamllab/awesome-game-ai, 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 README's opening to clarify repo's nature as a resource list

    Why:

    CURRENT
    A curated, but incomplete, list of game AI resources on **multi-agent** learning.
    COPY-PASTE FIX
    A curated and comprehensive list of **academic papers, open-source projects, and learning materials** focused on **multi-agent game AI**. This repository serves as a central hub for researchers, students, and enthusiasts exploring the field, rather than a development framework or library.
  • mediumhomepage#2
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Add a relevant URL to the 'Homepage' field in the repository settings (e.g., an organization page or a dedicated project page).
  • mediumtopics#3
    Add 'awesome-list' topic to reinforce repo type

    Why:

    CURRENT
    ai, awesome, game-ai, imperfect-information-games, multi-agent, reinforcement-learning
    COPY-PASTE FIX
    ai, awesome, awesome-list, game-ai, imperfect-information-games, multi-agent, reinforcement-learning

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 datamllab/awesome-game-ai
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PettingZoo
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PettingZoo · recommended 1×
  2. Unity ML-Agents Toolkit · recommended 1×
  3. OpenSpiel · recommended 1×
  4. Gymnasium (formerly OpenAI Gym) · recommended 1×
  5. MAgent · recommended 1×
  • CATEGORY QUERY
    What resources exist for developing AI in multi-agent game environments?
    you: not recommended
    AI recommended (in order):
    1. PettingZoo
    2. Unity ML-Agents Toolkit
    3. OpenSpiel
    4. Gymnasium (formerly OpenAI Gym)
    5. MAgent
    6. StarCraft II Learning Environment (SC2LE)
    7. Google Football Environment

    AI recommended 7 alternatives but never named datamllab/awesome-game-ai. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking frameworks and materials for reinforcement learning in competitive game scenarios.
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym (openai/gym)
    2. Gymnasium (Farama-Foundation/Gymnasium)
    3. Stable Baselines3 (DLR-RM/stable-baselines3)
    4. PettingZoo (Farama-Foundation/PettingZoo)
    5. AlphaStar (deepmind/pysc2)
    6. Unity ML-Agents (Unity-Technologies/ml-agents)
    7. RLlib (ray-project/ray)
    8. OpenSpiel (deepmind/open_spiel)

    AI recommended 8 alternatives but never named datamllab/awesome-game-ai. 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 datamllab/awesome-game-ai?
    pass
    AI did not name datamllab/awesome-game-ai — 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 datamllab/awesome-game-ai in production, what risks or prerequisites should they evaluate first?
    pass
    AI named datamllab/awesome-game-ai 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 datamllab/awesome-game-ai solve, and who is the primary audience?
    pass
    AI did not name datamllab/awesome-game-ai — 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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datamllab/awesome-game-ai — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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