RRepoGEO

REPOGEO REPORT · LITE

pfnet/pfrl

Default branch master · commit 3578e33e · scanned 6/28/2026, 6:21:31 PM

GitHub: 1,271 stars · 158 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 pfnet/pfrl, 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 paragraph to highlight core value

    Why:

    CURRENT
    PFRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using PyTorch.
    COPY-PASTE FIX
    PFRL is a research-grade deep reinforcement learning library, built on PyTorch, that provides robust, reproducible implementations of a wide range of state-of-the-art deep RL algorithms. It's designed for researchers and developers seeking a flexible and comprehensive toolkit for experimentation and development.
  • mediumhomepage#2
    Add the official documentation URL as the repository homepage

    Why:

    COPY-PASTE FIX
    http://pfrl.readthedocs.io/en/latest/

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 pfnet/pfrl
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DLR-RM/stable-baselines3
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DLR-RM/stable-baselines3 · recommended 2×
  2. ray-project/ray · recommended 2×
  3. vwxyzjn/cleanrl · recommended 2×
  4. thu-ml/tianshou · recommended 2×
  5. deepmind/acme · recommended 2×
  • CATEGORY QUERY
    What are the best Python libraries for implementing deep reinforcement learning algorithms?
    you: not recommended
    AI recommended (in order):
    1. Stable Baselines3 (DLR-RM/stable-baselines3)
    2. RLlib (ray-project/ray)
    3. CleanRL (vwxyzjn/cleanrl)
    4. Tianshou (thu-ml/tianshou)
    5. Keras-RL2 (keras-rl/keras-rl2)
    6. DeepMind's Acme (deepmind/acme)
    7. OpenAI Baselines (openai/baselines)

    AI recommended 7 alternatives but never named pfnet/pfrl. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a deep learning library for reinforcement learning experiments supporting diverse action types.
    you: not recommended
    AI recommended (in order):
    1. RLlib (ray-project/ray)
    2. Stable Baselines3 (DLR-RM/stable-baselines3)
    3. Tianshou (thu-ml/tianshou)
    4. Acme (deepmind/acme)
    5. CleanRL (vwxyzjn/cleanrl)

    AI recommended 5 alternatives but never named pfnet/pfrl. 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 pfnet/pfrl?
    pass
    AI named pfnet/pfrl explicitly

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

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

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

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pfnet/pfrl — 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