RRepoGEO

REPOGEO REPORT · LITE

astooke/rlpyt

Default branch master · commit f04f23db · scanned 6/25/2026, 2:13:05 PM

GitHub: 2,280 stars · 327 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)

3 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 astooke/rlpyt, 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
    Refine the README's opening paragraph to explicitly position as a research framework

    Why:

    CURRENT
    Modular, optimized implementations of common deep RL algorithms in PyTorch, with unified infrastructure supporting all three major families of model-free algorithms: policy gradient, deep-q learning, and q-function policy gradient. Intended to be a high-throughput code-base for small- to medium-scale research (large-scale meaning like OpenAI Dota with 100's GPUs).
    COPY-PASTE FIX
    rlpyt is a modular, high-throughput deep reinforcement learning *research framework* built in PyTorch. It offers optimized implementations of common RL algorithms, supporting policy gradient, deep-q learning, and q-function policy gradient, ideal for small- to medium-scale research.
  • mediumhomepage#2
    Add the ReadTheDocs URL as the repository homepage

    Why:

    COPY-PASTE FIX
    https://rlpyt.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 astooke/rlpyt
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. vwxyzjn/cleanrl · recommended 2×
  3. thu-ml/tianshou · recommended 2×
  4. DLR-RM/stable-baselines3 · recommended 1×
  5. pylessard/pytorch-drl · recommended 1×
  • CATEGORY QUERY
    What are some good PyTorch libraries for implementing deep reinforcement learning algorithms?
    you: not recommended
    AI recommended (in order):
    1. RLlib (ray-project/ray)
    2. Stable Baselines3 (SB3) (DLR-RM/stable-baselines3)
    3. CleanRL (vwxyzjn/cleanrl)
    4. Tianshou (thu-ml/tianshou)
    5. PyTorch-DRL (pylessard/pytorch-drl)

    AI recommended 5 alternatives but never named astooke/rlpyt. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a modular, high-throughput deep RL framework supporting multi-GPU training in PyTorch.
    you: not recommended
    AI recommended (in order):
    1. RLlib (ray-project/ray)
    2. CleanRL (vwxyzjn/cleanrl)
    3. Tianshou (thu-ml/tianshou)
    4. Acme (deepmind/acme)

    AI recommended 4 alternatives but never named astooke/rlpyt. 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 astooke/rlpyt?
    pass
    AI named astooke/rlpyt explicitly

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

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

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

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astooke/rlpyt — 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