REPOGEO REPORT · LITE

google-research/batch-ppo

Default branch master · commit 3d097059 · scanned 6/2/2026, 10:01:54 PM

GitHub: 977 stars · 148 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 google-research/batch-ppo, 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 README opening to emphasize TensorFlow and infrastructure role

Why:

CURRENT

Batch PPO This project provides optimized infrastructure for reinforcement learning. It extends the [OpenAI gym interface][post-gym] to multiple parallel environments and allows agents to be implemented in TensorFlow and perform batched computation. As a starting point, we provide BatchPPO, an optimized implementation of [Proximal Policy Optimization][post-ppo].

COPY-PASTE FIX

Batch PPO is a Google Research project providing optimized **TensorFlow-based infrastructure** for **efficient batched reinforcement learning**. It extends the [OpenAI gym interface][post-gym] to multiple parallel environments, enabling agents to perform batched computation. While it includes an optimized implementation of [Proximal Policy Optimization][post-ppo] (BatchPPO) as a starting point, its primary focus is on the underlying infrastructure.

mediumhomepage#2
Add a homepage URL
Why:
COPY-PASTE FIX
```
https://github.com/google-research/batch-ppo
```

mediumtopics#3

Expand topics to include parallel/distributed RL

Why:

CURRENT

artificial-intelligence, control, multi-processing, python, reinforcement-learning, tensorflow, vectorized-computation

COPY-PASTE FIX

artificial-intelligence, control, multi-processing, python, reinforcement-learning, tensorflow, vectorized-computation, parallel-reinforcement-learning, distributed-reinforcement-learning, batched-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 google-research/batch-ppo

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

ray-project/ray

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

ray-project/ray · recommended 2×
RLlib · recommended 1×
Stable Baselines3 · recommended 1×
Acme · recommended 1×
Tianshou · recommended 1×

CATEGORY QUERY
Looking for a Python library to perform efficient batched reinforcement learning experiments.
you: not recommended
AI recommended (in order):
1. RLlib
2. Stable Baselines3
3. Acme
4. Tianshou
5. CleanRL
AI recommended 5 alternatives but never named google-research/batch-ppo. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How to implement parallel reinforcement learning agents with vectorized environments for faster training?
you: not recommended
AI recommended (in order):
1. RLlib (ray-project/ray)
2. Ray (ray-project/ray)
3. Stable Baselines3 (DLR-RM/stable-baselines3)
4. CleanRL (vwxyzjn/cleanrl)
5. Gymnasium (Farama-Foundation/Gymnasium)
6. TorchRL (pytorch/rl)
7. Tianshou (thu-ml/tianshou)
8. Acme (deepmind/acme)
AI recommended 8 alternatives but never named google-research/batch-ppo. 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 google-research/batch-ppo?
pass
AI did not name google-research/batch-ppo — 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 google-research/batch-ppo in production, what risks or prerequisites should they evaluate first?
pass
AI named google-research/batch-ppo 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 google-research/batch-ppo solve, and who is the primary audience?
pass
AI did not name google-research/batch-ppo — 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?

Embed your GEO score

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/google-research/batch-ppo.svg)](https://repogeo.com/en/r/google-research/batch-ppo)

HTML

<a href="https://repogeo.com/en/r/google-research/batch-ppo"><img src="https://repogeo.com/badge/google-research/batch-ppo.svg" alt="RepoGEO" /></a>

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite