RRepoGEO

REPOGEO REPORT · LITE

PKU-MARL/HARL

Default branch main · commit b1af98b0 · scanned 6/7/2026, 12:42:31 AM

GitHub: 917 stars · 130 forks

AI VISIBILITY SCORE
30 /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
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 PKU-MARL/HARL, 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
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    multi-agent-reinforcement-learning, marl, reinforcement-learning, pytorch, heterogeneous-agents, deep-reinforcement-learning, harl, multi-agent-systems, machine-learning
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0) to clarify usage terms.
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    Set the repository homepage URL to the associated research paper, project website, or the GitHub repository URL itself if no dedicated page exists.

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/HARL
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 2×
  2. deepmind/open_spiel · recommended 2×
  3. pytorch/rl · recommended 2×
  4. DLR-RM/stable-baselines3 · recommended 2×
  5. Farama-Foundation/PettingZoo · recommended 1×
  • CATEGORY QUERY
    How to implement multi-agent reinforcement learning for diverse agents without parameter sharing?
    you: not recommended
    AI recommended (in order):
    1. RLlib (ray-project/ray)
    2. PettingZoo (Farama-Foundation/PettingZoo)
    3. OpenSpiel (deepmind/open_spiel)
    4. TorchRL (pytorch/rl)
    5. Stable Baselines3 (DLR-RM/stable-baselines3)
    6. CleanRL (vwxyzjn/cleanrl)

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

    Show full AI answer
  • CATEGORY QUERY
    What are good PyTorch libraries for heterogeneous multi-agent reinforcement learning algorithms?
    you: not recommended
    AI recommended (in order):
    1. RLlib (ray-project/ray)
    2. MARL-Algorithms (PyMARL) (oxwhirl/pymarl)
    3. OpenSpiel (deepmind/open_spiel)
    4. TorchRL (pytorch/rl)
    5. Stable Baselines3 (DLR-RM/stable-baselines3)
    6. PyTorch-MARL (by UCL) (uoe-agents/pytorch-marl)

    AI recommended 6 alternatives but never named PKU-MARL/HARL. 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/HARL?
    pass
    AI named PKU-MARL/HARL 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/HARL in production, what risks or prerequisites should they evaluate first?
    pass
    AI named PKU-MARL/HARL 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/HARL solve, and who is the primary audience?
    pass
    AI named PKU-MARL/HARL explicitly

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

Embed your GEO score

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MARKDOWN (README)
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PKU-MARL/HARL — 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