RRepoGEO

REPOGEO REPORT · LITE

haarnoja/sac

Default branch master · commit 8258e336 · scanned 5/27/2026, 6:43:03 AM

GitHub: 1,259 stars · 251 forks

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 haarnoja/sac, 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 deprecation notice to highlight historical value

    Why:

    CURRENT
    **This repository is no longer maintained. Please use our new Softlearning package instead.**
    COPY-PASTE FIX
    **This repository contains the original TensorFlow implementation of Soft Actor-Critic (SAC) from the ICML 2018 paper. While no longer actively maintained, it serves as a foundational reference. For ongoing development and a more comprehensive package, please refer to our Softlearning repository.**
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    reinforcement-learning, deep-learning, tensorflow, soft-actor-critic, sac, continuous-control, robotics, machine-learning
  • mediumlicense#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    Add a section to the README, e.g., '## License\nThis project's licensing terms are detailed in the `LICENSE` file. Please consult it for specific conditions, as it is not a standard SPDX license.'

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 haarnoja/sac
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Soft Actor-Critic (SAC)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Soft Actor-Critic (SAC) · recommended 1×
  2. Twin Delayed DDPG (TD3) · recommended 1×
  3. Proximal Policy Optimization (PPO) · recommended 1×
  4. Deep Deterministic Policy Gradients (DDPG) · recommended 1×
  5. Asynchronous Advantage Actor-Critic (A3C) · recommended 1×
  • CATEGORY QUERY
    What are effective reinforcement learning methods for continuous control environments?
    you: not recommended
    AI recommended (in order):
    1. Soft Actor-Critic (SAC)
    2. Twin Delayed DDPG (TD3)
    3. Proximal Policy Optimization (PPO)
    4. Deep Deterministic Policy Gradients (DDPG)
    5. Asynchronous Advantage Actor-Critic (A3C)
    6. Trust Region Policy Optimization (TRPO)

    AI recommended 6 alternatives but never named haarnoja/sac. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which deep reinforcement learning libraries support continuous action spaces using TensorFlow?
    you: not recommended
    AI recommended (in order):
    1. TF-Agents
    2. Stable Baselines3
    3. Keras-RL2
    4. RLlib
    5. TRFL (TensorFlow Reinforcement Learning)

    AI recommended 5 alternatives but never named haarnoja/sac. 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 haarnoja/sac?
    pass
    AI named haarnoja/sac explicitly

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

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

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

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haarnoja/sac — 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