Neural Network Meets Falling Sand Game
Neural Network Meets Falling Sand Game
Hey everyone! Today, I'm excited to share my progress on a falling sand game powered by neural networks. Let's dive into the evolution of this project and the fascinating challenges I've encountered along the way.
The Concept
This project combines the classic falling sand game mechanics with the power of neural networks. Instead of using traditional rule-based physics, the game uses trained neural networks to determine how particles interact with each other and their environment.
How It Works
Traditional Falling Sand
In a classic falling sand game:
- Particles follow predefined rules
- Sand falls down, water flows, fire rises
- Each element type has hardcoded behavior
- A neural network learns particle behavior from examples
- The network predicts how particles should move
- Emergent behaviors can arise from training
- The physics can be more organic and surprising
- Engine: Pygame for rendering and interaction
- Neural Network: Trained to predict particle movements
- Training Data: Generated from observed particle interactions
- Training the network to handle edge cases
- Balancing performance with accuracy
- Getting emergent behavior that's still playable
- Source: https://github.com/ruapotato/AISand
- Blog: https://www.hackers-game.com/2024/09/09/neural-network-meets-falling-sand-game/
Neural Network Approach
In this version:
Technical Implementation
Challenges Encountered
Creating a neural network-based physics system presents unique challenges:
Source Code
The project is available on GitHub for those interested in exploring AI-powered game development: