Autonomous Driving Comparison Between Neat and Hyperneat Using Pygame

Abstract

The use of autonomous driving is growing in popularity thanks to manufacturers like Tesla, Waymo, and numerous others. Regardless of autonomous driving, the application of the neuroevolution in neural networks today is without a doubt one of the most important and ground-breaking areas of research. A neural network's training, evolution, mutation, and crossover processes are both enjoyable and essential for the future. For the greatest results in this study, we created a neural network, trained it using the NEAT neuroevolution algorithm, performed mutation and crossover, and then managed the extinction of our genome. The NeuroEvolution of Augmenting Topologies (NEAT) and HyperNEAT brain evolution algorithms are both promising. We compare the performance of these two algorithms on a benchmark problem of a car driving simulator that includes strategic decision making. Our results demonstrate where HyperNEAT surpasses NEAT and where NEAT outperforms HyperNEAT. In this study, the neural network is primarily used to learn how to navigate a track on its own. To make it more challenging and learn how it performs on various tracks, we can test and train its performance on various tracks. That's when elitism and other mutational configurations come in help. Sometimes it works better to create a more random mutation, other times not so much. The same is true of elitism; it is beneficial to some extent but loses its significance beyond that. We discovered a good setup that allows our network to function at its optimum but varies slightly from track to track. There is always room for a program to perform additional tasks and to output intelligence in a real-time setting.