RRepoGEO

REPOGEO REPORT · LITE

chainer/chainerrl

Default branch master · commit 7eed3756 · scanned 6/29/2026, 6:27:11 PM

GitHub: 1,200 stars · 226 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
28 /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
2 / 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 chainer/chainerrl, 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
    Clarify project status and recommended path in README

    Why:

    CURRENT
    ChainerRL (this repository) is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Chainer, a flexible deep learning framework. PFRL is the PyTorch analog of ChainerRL.
    COPY-PASTE FIX
    ChainerRL (this repository) is a deep reinforcement learning library built on Chainer. **Important: Chainer is no longer actively developed.** For new projects, we strongly recommend using **PFRL**, its PyTorch analog, which provides similar functionalities with an actively maintained backend. ChainerRL is primarily maintained for existing projects or specific research requiring the Chainer framework.
  • mediumhomepage#2
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/preferred-homepage-for-chainerrl-or-pfrl
  • lowtopics#3
    Add successor project topics

    Why:

    CURRENT
    actor-critic, chainer, deep-learning, dqn, machine-learning, python, reinforcement-learning
    COPY-PASTE FIX
    actor-critic, chainer, deep-learning, dqn, machine-learning, pfrl, python, pytorch, 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 chainer/chainerrl
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. thu-ml/tianshou · recommended 1×
  4. deepmind/acme · recommended 1×
  5. vwxyzjn/cleanrl · recommended 1×
  • CATEGORY QUERY
    What Python libraries are available for implementing deep reinforcement learning algorithms?
    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)
    6. Dopamine (google/dopamine)

    AI recommended 6 alternatives but never named chainer/chainerrl. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a Python framework to develop advanced deep reinforcement learning models like DQN.
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow (with Keras API)
    3. JAX
    4. RLlib
    5. Stable Baselines3
    6. Acme

    AI recommended 6 alternatives but never named chainer/chainerrl. 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 chainer/chainerrl?
    pass
    AI did not name chainer/chainerrl — 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 chainer/chainerrl in production, what risks or prerequisites should they evaluate first?
    pass
    AI named chainer/chainerrl 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 chainer/chainerrl solve, and who is the primary audience?
    pass
    AI named chainer/chainerrl 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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