NN-Racer
Completed
Neural network evolution using genetic algorithms for autonomous racing car behavior.
Date
2024-05
Duration
2 months
Team
solo
Difficulty
hard
Project Story
NN-Racer is an experiment in evolutionary computation where neural networks learn to control racing cars through genetic algorithms. The cars improve their performance over generations through mutation and selection.
Neural network cars evolving racing behavior
The project demonstrates how genetic algorithms can train neural networks without traditional backpropagation, evolving behavior through fitness-based selection and random mutations of network weights and biases.
Technical Details
Tech Stack
Python TensorFlow Genetic Algorithms Neural Networks Simulation
Key Features
✓ Neural network evolution
✓ Genetic algorithm optimization
✓ Autonomous behavior learning
✓ Performance tracking
✓ Visualization of evolution
✓ Multi-objective optimization
Challenges Faced
⚠ Computational intensity of evolution
⚠ Hyperparameter tuning complexity
⚠ Local optima problems
⚠ Balancing exploration vs. exploitation
Key Learnings
💡 Genetic algorithms can train complex behaviors
💡 Neural network architecture affects learning
💡 Fitness function design is crucial
💡 Evolution can find unexpected solutions
💡 Performance improvements over generations