RRepoGEO

REPOGEO REPORT · LITE

nikitasrivatsan/DeepLearningVideoGames

Default branch master · commit ec84d554 · scanned 5/27/2026, 6:57:46 AM

GitHub: 1,091 stars · 216 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 nikitasrivatsan/DeepLearningVideoGames, 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
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    Implementations of Deep Q Networks (DQN) and other deep reinforcement learning algorithms applied to learn strategies for classic video games like Pong and Tetris from raw pixel input. Ideal for students and researchers exploring AI game control.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    deep-reinforcement-learning, dqn, reinforcement-learning, deep-learning, video-games, ai, machine-learning, game-ai, pong, tetris, atari
  • mediumreadme#3
    Add a clear introductory sentence to the README

    Why:

    CURRENT
    # Using Deep Q Networks to Learn Video Game Strategies
#### Nikita Srivatsan, Ivan Kuznetsov, Willis Wang

## 1. Abstract

In this project, we apply a deep learning model recently developed by Minh et al 2015 [1] to learn optimal control patterns from visual input using reinforcement learning.
    COPY-PASTE FIX
    # Using Deep Q Networks to Learn Video Game Strategies

This repository provides implementations of Deep Q Networks (DQN) and other deep reinforcement learning algorithms to learn optimal control patterns for classic video games like Pong and Tetris from raw pixel input.

#### Nikita Srivatsan, Ivan Kuznetsov, Willis Wang

## 1. Abstract

In this project, we apply a deep learning model recently developed by Minh et al 2015 [1] to learn optimal control patterns from visual input using 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 nikitasrivatsan/DeepLearningVideoGames
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 3×
  2. openai/gym · recommended 2×
  3. Unity-Technologies/ml-agents · recommended 2×
  4. mwydmuch/ViZDoom · recommended 2×
  5. DLR-RM/stable-baselines3 · recommended 2×
  • CATEGORY QUERY
    How can I build an AI to learn video game strategies from screen pixels?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym (openai/gym)
    2. Gym-Retro (openai/gym-retro)
    3. mss (python-mss/mss)
    4. Pillow (python-pillow/Pillow)
    5. pynput (moses-palmer/pynput)
    6. vgamepad (yannbouteiller/vgamepad)
    7. Cheat Engine
    8. pymem (gamemaker1/pymem)
    9. Unity ML-Agents (Unity-Technologies/ml-agents)
    10. ViZDoom (mwydmuch/ViZDoom)
    11. PyTorch (pytorch/pytorch)
    12. TensorFlow (tensorflow/tensorflow)
    13. Keras API (keras-team/keras)
    14. JAX (google/jax)
    15. Stable Baselines3 (DLR-RM/stable-baselines3)
    16. RLlib (ray-project/ray)
    17. CleanRL (vwxyzjn/cleanrl)
    18. OpenCV (cv2) (opencv/opencv-python)
    19. NumPy (numpy/numpy)

    AI recommended 19 alternatives but never named nikitasrivatsan/DeepLearningVideoGames. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for deep reinforcement learning examples applied to game control using visual data.
    you: not recommended
    AI recommended (in order):
    1. DeepMind's DQN
    2. Stable Baselines3 (DLR-RM/stable-baselines3)
    3. Gymnasium (Farama-Foundation/Gymnasium)
    4. OpenAI Gym (openai/gym)
    5. RLlib (ray-project/ray)
    6. Ray (ray-project/ray)
    7. Unity ML-Agents Toolkit (Unity-Technologies/ml-agents)
    8. Unity
    9. Minigrid (Farama-Foundation/Minigrid)
    10. ViZDoom (mwydmuch/ViZDoom)
    11. OpenSpiel (deepmind/open_spiel)

    AI recommended 11 alternatives but never named nikitasrivatsan/DeepLearningVideoGames. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 nikitasrivatsan/DeepLearningVideoGames?
    pass
    AI named nikitasrivatsan/DeepLearningVideoGames explicitly

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

  • If a team adopts nikitasrivatsan/DeepLearningVideoGames in production, what risks or prerequisites should they evaluate first?
    pass
    AI named nikitasrivatsan/DeepLearningVideoGames 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 nikitasrivatsan/DeepLearningVideoGames solve, and who is the primary audience?
    pass
    AI did not name nikitasrivatsan/DeepLearningVideoGames — 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 nikitasrivatsan/DeepLearningVideoGames. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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nikitasrivatsan/DeepLearningVideoGames — 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