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Fabricio Viskor
Fabricio Viskor

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NitroGen — Vision-to-Action Game AI

#gamedev #agents #deeplearning #ai

NitroGen is an open research project from the MineDojo ecosystem exploring a simple but powerful idea:

An AI can learn to play games by looking at the screen and imitating human actions — without access to the game engine, APIs, or internal state.

This repository provides a reference implementation of a vision-to-action game-playing agent trained via imitation learning.

Key Idea

Instead of reinforcement learning and reward engineering, NitroGen uses behavior cloning from real human gameplay videos.

The model learns a direct mapping:

screen pixels → neural network → controller actions
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This allows the same agent architecture to work across many different games.

What Problems Does NitroGen Solve?

Traditional game AI often requires:

  • game engine access
  • internal state or memory reading
  • custom APIs or SDKs
  • handcrafted reward functions

NitroGen avoids all of the above.

Advantages

  • Engine-agnostic
  • Game-agnostic
  • No reward functions
  • Faster experimentation than RL
  • Human-like behavior

How NitroGen Works (High Level)

  1. Collect gameplay videos from real human players
  2. Extract player actions from controller overlays
  3. Train a vision-based neural network via imitation learning
  4. Predict the next action given the current frame

The result is a general game-playing agent that operates purely from visual input.

Use Cases

Legitimate use cases include:

  • Game AI research
  • Imitation learning experiments
  • Multi-game agents
  • Automated game testing (QA)
  • Accessibility tools
  • Embodied AI research
  • Education and ML courses

⚠️ This project is not intended for cheating, farming, or online exploitation.

Repository Structure 

nitrogen/
├── models/        # Vision-to-action models
├── datasets/      # Gameplay datasets
├── envs/          # Game wrappers / environments
├── scripts/       # Training and evaluation scripts
├── configs/       # Experiment configurations
└── checkpoints/   # Pretrained weights (if available)
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Installation

Requirements

  • Python 3.9+
  • Linux or macOS recommended
  • GPU strongly recommended

Setup

Clone the repository: https://github.com/MineDojo/NitroGen

git clone https://github.com/minedojo/nitrogen.git
cd nitrogen
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Create a virtual environment:

python -m venv venv
source venv/bin/activate
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Install dependencies:

pip install -r requirements.txt
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Running a Pretrained Model

If pretrained checkpoints are available:

python scripts/run_agent.py \
  --config configs/eval.yaml \
  --checkpoint checkpoints/nitrogen.pt
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The agent will:

  • receive game frames
  • predict controller actions
  • interact with the environment in real time

Training

Minimal training example:

python scripts/train.py \
  --config configs/train.yaml
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Key parameters to tune:

  • frame resolution
  • action space
  • sequence length
  • dataset quality

Training is significantly more stable than reinforcement learning approaches.

Limitations

  • Performance depends heavily on data quality
  • No long-term planning by default
  • Limited temporal reasoning
  • Not optimized for competitive or online play

NitroGen is a foundation, not a finished product.

Why This Project Matters

NitroGen provides:

  • A clean reference implementation of vision-to-action agents
  • A scalable alternative to reinforcement learning
  • A practical starting point for general game AI research

If you want to experiment with game AI without touching the game engine, this project is a strong base.

Get Involved

If this project is useful to you:

  • ⭐ Star the repository
  • 🧪 Run experiments
  • 🐛 Open issues
  • 🔧 Build on top of it

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