Yet Another Simulation

A WebAssembly-powered neural network battle simulation. Watch AI agents trained with NEAT evolution battle each other in real-time.

Quick Start (2 minutes)

Prerequisites

# Install Rust and wasm-pack
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
source ~/.cargo/env
cargo install wasm-pack

# Install Node.js (any recent version)
# macOS: brew install node
# Or download from: https://nodejs.org/

Run the Simulation

git clone <your-repo>
cd yet_another_simulation

# Install dependencies and start
npm install
npm start

# Open http://localhost:8000
# Select a trained model from the dropdown and watch the battle!

That's it! The repository includes 58 pre-trained AI models ready to use.

What You'll See

• Colored dots: Each represents an AI agent with neural network decision-making
• Model dropdown: Different AI models trained with various parameters
• Real-time combat: Agents use weapons, steering behaviors, and strategy
• Team battles: 1v1, 2v2, 3v3, or 4v4 configurations
• Elo ratings: Models ranked by tournament performance

Current Scripts (All Working)

For Users

npm start                        # Run the simulation
npm test                         # Run UI tests
node scripts/generate_runs.js    # Refresh model catalog

For Training New Models

node scripts/run_experiments.js      # Train multiple model variants (~hours)
node scripts/run_tournaments.js      # Run tournaments on trained models
node scripts/run_global_tournament.js # Cross-parameter model comparison
node scripts/compare_fitness_variants.js # Compare different fitness functions

File Structure (What Matters)

├── index.html           # Simulation interface
├── script.js            # Frontend logic  
├── wasm/pkg/           # Compiled WASM module (pre-built)
├── sim_core/out/       # 58 trained models (ready to use)
│   ├── runs.json       # Model catalog for frontend
│   └── */              # Individual model directories
└── scripts/            # Working automation scripts

Understanding the Models

Each model in the dropdown shows:

• Team size: 1v1, 2v2, 3v3, 4v4
• Duration: 30s, 60s, 120s battle length used for training
• Fitness function: What the AI optimized for
  • health-plus-damage: Survival + combat effectiveness
  • health-damage-salvage: Above + resource collection
  • health-damage-explore: Above + map exploration
• Elo rating: Performance against other models

Try different models to see how training parameters affect behavior!

Training Your Own Models

The existing training system works but takes time:

# Full training suite (creates many model variants)
node scripts/run_experiments.js

# This will:
# - Train 1v1, 2v2, 3v3, 4v4 team configurations  
# - Use 30s and 60s battle durations
# - Try different fitness functions
# - Take several hours total
# - Output to sim_core/out/<model-name>/

After training, update the model catalog:

node scripts/run_tournaments.js     # Generate Elo ratings
node scripts/generate_runs.js       # Update frontend catalog

Troubleshooting

Models not loading: Run node scripts/generate_runs.js Port 8000 busy: Use npm start -- --port=8001
Build issues: The WASM module is pre-built, but if needed: cd sim_core && wasm-pack build --target web --out-dir ../wasm/pkg

Technical Details

• Rust WASM core: High-performance simulation engine
• NEAT evolution: Topology-evolving neural networks
• Real-time visualization: Canvas-based rendering
• Model persistence: JSON format for easy sharing
• Tournament system: Elo rating-based model comparison

The simulation demonstrates emergent AI behavior where simple rules create complex strategy.

Screenshots