RRepoGEO

REPOGEO REPORT · LITE

Omegastick/pytorch-cpp-rl

Default branch master · commit e25fa641 · scanned 6/9/2026, 10:33:13 AM

GitHub: 534 stars · 89 forks

AI VISIBILITY SCORE
22 /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
1 / 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 Omegastick/pytorch-cpp-rl, 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
    Reposition the README's opening to emphasize 'production-ready C++ RL framework'

    Why:

    CURRENT
    CppRl is a reinforcement learning framework, written using the PyTorch C++ frontend. It is *very* heavily based on Ikostrikov's wonderful pytorch-a2c-ppo-acktr-gail. You could even consider this a port.
    COPY-PASTE FIX
    **CppRl is a production-ready reinforcement learning framework, built from the ground up using the PyTorch C++ frontend (LibTorch).** While inspired by Ikostrikov's `pytorch-a2c-ppo-acktr-gail`, CppRl is a distinct, extensible C++ implementation designed for high-performance applications where Python is not viable.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add a relevant URL (e.g., a project website, documentation, or a dedicated landing page) to the 'Homepage' field in the repository settings.
  • mediumreadme#3
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Comparison' or 'Why CppRl?' that explicitly differentiates this framework from common alternatives like Python-based RL libraries (e.g., RLlib, OpenAI Gym) and generic C++ libraries (e.g., LibTorch, ONNX Runtime), highlighting its unique position as a dedicated C++ RL framework.

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 Omegastick/pytorch-cpp-rl
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pytorch/pytorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/pytorch · recommended 2×
  2. OpenAI Gym · recommended 1×
  3. TensorFlow Lite · recommended 1×
  4. ONNX Runtime · recommended 1×
  5. RLlib · recommended 1×
  • CATEGORY QUERY
    Seeking a C++ reinforcement learning framework for production applications where Python is not viable.
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym
    2. TensorFlow Lite
    3. ONNX Runtime
    4. RLlib
    5. DeepMind Lab
    6. LibTorch
    7. TensorFlow C++ API

    AI recommended 7 alternatives but never named Omegastick/pytorch-cpp-rl. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good options for implementing A2C or PPO algorithms using PyTorch in C++?
    you: not recommended
    AI recommended (in order):
    1. LibTorch (pytorch/pytorch)
    2. ONNX Runtime (microsoft/onnxruntime)
    3. TorchScript (pytorch/pytorch)
    4. Eigen (eigenteam/eigen-git-mirror)
    5. Blaze (blaze-lib/blaze)
    6. Stable Baselines3 (DLR-RM/stable-baselines3)
    7. Ray RLlib (ray-project/ray)
    8. TensorFlow Lite (tensorflow/tensorflow)
    9. MXNet C++ API (apache/mxnet)

    AI recommended 9 alternatives but never named Omegastick/pytorch-cpp-rl. 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 Omegastick/pytorch-cpp-rl?
    pass
    AI did not name Omegastick/pytorch-cpp-rl — 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 Omegastick/pytorch-cpp-rl in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Omegastick/pytorch-cpp-rl 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 Omegastick/pytorch-cpp-rl solve, and who is the primary audience?
    pass
    AI did not name Omegastick/pytorch-cpp-rl — 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?

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