Tutorial on Training a Self-Parking Car Using a Genetic Algorithm

Introduction to Self-Parking with Genetic Algorithms

This tutorial outlines the process of training a simulated car to self-park using a genetic algorithm. The core idea involves evolving a car's 'genome' to optimize its parking behavior over successive generations.

The Evolution Process

Initially, cars are created with random genomes, leading to uncoordinated movements. Through generations, the genetic algorithm refines these genomes, allowing cars to learn and improve their parking capabilities. By approximately the 40th generation, cars begin to approach and position themselves closer to the parking spot, demonstrating a clear learning curve.

Simulator Features and Implementation

A web-based simulator is provided, enabling users to observe the evolution process, train cars from scratch, adjust genetic parameters, and view trained cars in action. Users can also manually attempt to park a car within the simulator. The genetic algorithm itself is implemented in TypeScript, with the source code available for review.

Car Components and Genetic Evolution

To facilitate self-parking, the simulated car is equipped with 'muscles' (engine, steering wheel) for movement and 'eyes' (sensors) for obstacle detection. A 'brain' function, which maps sensor inputs to movement outputs, is then evolved using the genetic algorithm. This process optimizes the 180-bit 'car genome' to achieve efficient parking maneuvers.