REPOGEO REPORT · LITE
ShangtongZhang/DeepRL
Default branch master · commit c0968b5c · scanned 5/17/2026, 3:01:48 PM
GitHub: 3,425 stars · 697 forks
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.
2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
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 ShangtongZhang/DeepRL, 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.
- highreadme#1Reposition README opening to clearly state purpose as an RL experimentation framework
Why:
CURRENTModularized implementation of popular deep RL algorithms in PyTorch. Easy switch between toy tasks and challenging games.
COPY-PASTE FIXDeepRL is a modular PyTorch framework providing clean, runnable implementations of popular deep reinforcement learning algorithms. It's designed for researchers and practitioners to easily experiment with and compare various RL methods across toy tasks and challenging games.
- mediumtopics#2Add topics emphasizing framework and library aspects
Why:
CURRENTa2c, categorical-dqn, ddpg, deep-reinforcement-learning, deeprl, double-dqn, dqn, dueling-network-architecture, option-critic, option-critic-architecture, ppo, prioritized-experience-replay, pytorch, quantile-regression, rainbow, td3
COPY-PASTE FIXa2c, categorical-dqn, ddpg, deep-reinforcement-learning, deeprl, double-dqn, dqn, dueling-network-architecture, option-critic, option-critic-architecture, ppo, prioritized-experience-replay, pytorch, quantile-regression, rainbow, td3, reinforcement-learning-framework, rl-library, pytorch-rl
- lowreadme#3Add a sentence highlighting the ease of comparing algorithms
Why:
CURRENTEasy switch between toy tasks and challenging games.
COPY-PASTE FIXIt's designed for easy switching between toy tasks and challenging games, making it ideal for comparing the performance and characteristics of different algorithms.
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.
- CleanRL · recommended 2×
- RLlib · recommended 2×
- Stable Baselines3 · recommended 2×
- Tianshou · recommended 2×
- Catalyst.RL · recommended 1×
- CATEGORY QUERYSeeking a modular PyTorch framework to experiment with common deep reinforcement learning algorithms.you: not recommendedAI recommended (in order):
- CleanRL
- RLlib
- Stable Baselines3
- Tianshou
- Catalyst.RL
AI recommended 5 alternatives but never named ShangtongZhang/DeepRL. This is the gap to close.
Show full AI answer
- CATEGORY QUERYHow can I quickly compare performance of different deep Q-learning variants in PyTorch?you: not recommendedAI recommended (in order):
- Stable Baselines3
- RLlib
- CleanRL
- TorchRL
- Tianshou
AI recommended 5 alternatives but never named ShangtongZhang/DeepRL. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
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 ShangtongZhang/DeepRL?passAI named ShangtongZhang/DeepRL explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts ShangtongZhang/DeepRL in production, what risks or prerequisites should they evaluate first?passAI named ShangtongZhang/DeepRL 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 ShangtongZhang/DeepRL solve, and who is the primary audience?passAI did not name ShangtongZhang/DeepRL — 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
Drop this badge into the README of ShangtongZhang/DeepRL. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.
Live preview[](https://repogeo.com/en/r/ShangtongZhang/DeepRL)<a href="https://repogeo.com/en/r/ShangtongZhang/DeepRL"><img src="https://repogeo.com/badge/ShangtongZhang/DeepRL.svg" alt="RepoGEO" /></a>Subscribe to Pro for deep diagnoses
ShangtongZhang/DeepRL — 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