REPOGEO REPORT · LITE
TianhongDai/reinforcement-learning-algorithms
Default branch master · commit aad88625 · scanned 6/6/2026, 4:28:00 PM
GitHub: 694 stars · 110 forks
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 TianhongDai/reinforcement-learning-algorithms, 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.
- highlicense#1Add a standard open-source LICENSE file
Why:
CURRENT(no LICENSE file detected — the repo has no recognizable license)
COPY-PASTE FIXAdd a LICENSE file (e.g., MIT, Apache-2.0) to the repository root to clearly state the terms of use.
- mediumreadme#2Strengthen README's opening to highlight learning focus
Why:
CURRENTThis repository will implement the classic deep reinforcement learning algorithms by using **PyTorch**. The aim of this repository is to provide clear code for people to learn the deep reinforcemen learning algorithms.
COPY-PASTE FIXThis repository offers **exceptionally clear and learning-focused PyTorch implementations** of classic deep reinforcement learning algorithms, making it an ideal resource for students and researchers to understand and experiment with core concepts like DQN, DDPG, SAC, A2C, PPO, and TRPO.
- lowhomepage#3Add a homepage URL in repository settings
Why:
COPY-PASTE FIXSet the repository homepage URL in the GitHub settings to a relevant project page, documentation, or personal portfolio link.
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/cleanrl · recommended 1×
- ikostrikov/pytorch-rl · recommended 1×
- DLR-RM/stable-baselines3 · recommended 1×
- mgbellemare/MinimalRL · recommended 1×
- higgsfield/RL-Adventure · recommended 1×
- CATEGORY QUERYWhere can I find clear PyTorch implementations for common deep reinforcement learning algorithms?you: not recommendedAI recommended (in order):
- CleanRL (cleanrl/cleanrl)
- PyTorch-RL (ikostrikov/pytorch-rl)
- Stable Baselines3 (DLR-RM/stable-baselines3)
- Minimal RL (mgbellemare/MinimalRL)
- RL-Adventure (higgsfield/RL-Adventure)
- Deep Reinforcement Learning Hands-On (PacktPublishing/Deep-Reinforcement-Learning-Hands-On)
AI recommended 6 alternatives but never named TianhongDai/reinforcement-learning-algorithms. This is the gap to close.
Show full AI answer
- CATEGORY QUERYSeeking a collection of popular deep reinforcement learning algorithms implemented in PyTorch.you: not recommendedAI recommended (in order):
- Stable Baselines3
- CleanRL
- RLlib
- Tianshou
- PyTorch-DRL (pytorch/examples)
AI recommended 5 alternatives but never named TianhongDai/reinforcement-learning-algorithms. 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 TianhongDai/reinforcement-learning-algorithms?passAI named TianhongDai/reinforcement-learning-algorithms explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts TianhongDai/reinforcement-learning-algorithms in production, what risks or prerequisites should they evaluate first?passAI named TianhongDai/reinforcement-learning-algorithms 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 TianhongDai/reinforcement-learning-algorithms solve, and who is the primary audience?passAI did not name TianhongDai/reinforcement-learning-algorithms — 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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TianhongDai/reinforcement-learning-algorithms — 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