RRepoGEO

REPOGEO REPORT · LITE

rail-berkeley/rlkit

Default branch master · commit ac45a9db · scanned 5/15/2026, 1:12:26 AM

GitHub: 2,899 stars · 571 forks

AI VISIBILITY SCORE
35 /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
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 rail-berkeley/rlkit, 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 the README's opening statement to emphasize research focus

    Why:

    CURRENT
    # RLkit
Reinforcement learning framework and algorithms implemented in PyTorch.
    COPY-PASTE FIX
    # RLkit
RLkit is a comprehensive PyTorch-based framework for state-of-the-art reinforcement learning research, providing robust implementations of numerous off-policy and meta-RL algorithms.
  • mediumhomepage#2
    Add a project homepage URL

    Why:

    COPY-PASTE FIX
    https://rail-berkeley.github.io/rlkit/

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 rail-berkeley/rlkit
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 1×
  2. DLR-RM/stable-baselines3 · recommended 1×
  3. vwxyzjn/cleanrl · recommended 1×
  4. thu-ml/tianshou · recommended 1×
  5. pytorch/rl · recommended 1×
  • CATEGORY QUERY
    Seeking a robust reinforcement learning framework implemented in PyTorch for research.
    you: not recommended
    AI recommended (in order):
    1. RLlib (ray-project/ray)
    2. Stable Baselines3 (DLR-RM/stable-baselines3)
    3. CleanRL (vwxyzjn/cleanrl)
    4. Tianshou (thu-ml/tianshou)
    5. TorchRL (pytorch/rl)

    AI recommended 5 alternatives but never named rail-berkeley/rlkit. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective Python toolkits for developing and evaluating deep reinforcement learning agents?
    you: not recommended
    AI recommended (in order):
    1. RLlib
    2. Stable Baselines3
    3. CleanRL
    4. Tianshou
    5. Acme

    AI recommended 5 alternatives but never named rail-berkeley/rlkit. 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 rail-berkeley/rlkit?
    pass
    AI named rail-berkeley/rlkit explicitly

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

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