This simulation demonstrates how AI can learn to drive using genetic algorithms and neural networks. Cars must navigate randomly generated tracks without any prior knowledge of the environment.
Key Improvements:
Enhanced Neural Network: Using Sigmoid activation function for smoother decision making
Crossover: Combining the best traits from parent models
Adaptive Mutation: Automatically adjusts as generations progress
Performance Optimization: Delta-time based updates for consistent simulation
Improved Collision Detection: More accurate polygon-based detection