RRepoGEO

REPOGEO REPORT · LITE

openai/imitation

Default branch master · commit 8a2ed905 · scanned 6/12/2026, 7:57:50 PM

GitHub: 730 stars · 188 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 openai/imitation, 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
  • hightopics#1
    Add specific, relevant topics to improve categorization

    Why:

    CURRENT
    paper
    COPY-PASTE FIX
    imitation-learning, reinforcement-learning, gail, adversarial-learning, deep-learning, python
  • highreadme#2
    Reposition the README's opening to highlight its function as a library

    Why:

    CURRENT
    **Status:** Archive (code is provided as-is, no updates expected)

Generative Adversarial Imitation Learning
Jonathan Ho and Stefano Ermon
Contains an implementation of Trust Region Policy Optimization (Schulman et al., 2015).
    COPY-PASTE FIX
    **Status:** Archive (code is provided as-is, no updates expected)

This repository provides a clean, modular implementation of Generative Adversarial Imitation Learning (GAIL) and related algorithms, as described in the paper by Jonathan Ho and Stefano Ermon. It serves as a foundational library for training reinforcement learning agents from expert demonstrations, including an implementation of Trust Region Policy Optimization (Schulman et al., 2015).
  • mediumabout#3
    Update the repository description to emphasize its role as an implementation library

    Why:

    CURRENT
    Code for the paper "Generative Adversarial Imitation Learning"
    COPY-PASTE FIX
    A modular Python library implementing Generative Adversarial Imitation Learning (GAIL) and related algorithms for training reinforcement learning agents from expert demonstrations.

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 openai/imitation
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GAIL
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GAIL · recommended 1×
  2. AIRL · recommended 1×
  3. DAC · recommended 1×
  4. GCL · recommended 1×
  5. SQIL · recommended 1×
  • CATEGORY QUERY
    How can I train a policy from expert demonstrations using adversarial learning techniques?
    you: not recommended
    AI recommended (in order):
    1. GAIL
    2. AIRL
    3. DAC
    4. GCL
    5. SQIL
    6. PyTorch
    7. TensorFlow
    8. Stable Baselines3
    9. RLlib

    AI recommended 9 alternatives but never named openai/imitation. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for Python libraries to implement imitation learning for robotic control tasks.
    you: not recommended
    AI recommended (in order):
    1. Stable Baselines3 (DLR-RM/stable-baselines3)
    2. PyTorch-Imitation (HumanCompatibleAI/imitation)
    3. RLPyT (astooke/rlpyt)
    4. Acme (deepmind/acme)
    5. TensorFlow Agents (tensorflow/agents)
    6. Robotics Operating System (ROS) (ros/ros)
    7. MoveIt! (ros-planning/moveit)
    8. Keras (keras-team/keras)
    9. PyTorch (pytorch/pytorch)

    AI recommended 9 alternatives but never named openai/imitation. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 openai/imitation?
    pass
    AI named openai/imitation explicitly

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

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

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

Embed your GEO score

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite