REPOGEO REPORT · LITE

nikhilbarhate99/PPO-PyTorch

Default branch master · commit 728cce83 · scanned 5/17/2026, 7:52:56 AM

GitHub: 2,349 stars · 423 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)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

28 /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

2 / 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 nikhilbarhate99/PPO-PyTorch, 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

Strengthen README's opening to highlight beginner-friendly features

Why:

CURRENT

The README starts with '# PPO-PyTorch' followed by an update section, with the main introduction appearing later.

COPY-PASTE FIX

Add this sentence immediately after the '# PPO-PyTorch' heading:

A minimal, beginner-friendly PyTorch implementation of Proximal Policy Optimization (PPO) for deep reinforcement learning, featuring built-in logging and plotting utilities.

mediumtopics#2

Correct typo in 'pytorch-implmention' topic

Why:

CURRENT

pytorch-implmention

COPY-PASTE FIX

deep-learning, deep-reinforcement-learning, policy-gradient, ppo, ppo-pytorch, proximal-policy-optimization, pytorch, pytorch-implementation, pytorch-tutorial, reinforcement-learning, reinforcement-learning-algorithms

lowhomepage#3

Add a homepage URL to the repository metadata

Why:

COPY-PASTE FIX

https://colab.research.google.com/github/nikhilbarhate99/PPO-PyTorch/blob/master/PPO_colab.ipynb

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 nikhilbarhate99/PPO-PyTorch

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×
CleanRL · recommended 1×
Stable Baselines3 · recommended 1×
ikostrikov/pytorch-rl · recommended 1×
pytorch/examples · recommended 1×

CATEGORY QUERY
I need a clear PyTorch implementation of Proximal Policy Optimization for deep reinforcement learning.
you: not recommended
AI recommended (in order):
1. CleanRL
2. Stable Baselines3
3. PyTorch-RL (ikostrikov/pytorch-rl)
4. Minimal PPO (pytorch/examples)
5. spinningup (openai/spinningup)
6. RL-Baselines-Zoo (DLR-RM/rl-baselines3-zoo)
AI recommended 6 alternatives but never named nikhilbarhate99/PPO-PyTorch. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are beginner-friendly PyTorch PPO implementations for reinforcement learning with logging and plotting?
you: not recommended
AI recommended (in order):
1. CleanRL (cleanrl/cleanrl)
2. TensorBoard (tensorflow/tensorboard)
3. Stable Baselines3 (DLR-RM/stable-baselines3)
4. RLlib (ray-project/ray)
5. Ray (ray-project/ray)
6. Weights & Biases (wandb/wandb)
7. MLflow (mlflow/mlflow)
8. PyTorch (pytorch/pytorch)
9. Hugging Face TRL (huggingface/trl)
10. Hugging Face Transformers (huggingface/transformers)
AI recommended 10 alternatives but never named nikhilbarhate99/PPO-PyTorch. 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 nikhilbarhate99/PPO-PyTorch?
pass
AI named nikhilbarhate99/PPO-PyTorch explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts nikhilbarhate99/PPO-PyTorch in production, what risks or prerequisites should they evaluate first?
pass
AI named nikhilbarhate99/PPO-PyTorch 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 nikhilbarhate99/PPO-PyTorch solve, and who is the primary audience?
pass
AI did not name nikhilbarhate99/PPO-PyTorch — 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)

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nikhilbarhate99/PPO-PyTorch — 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