C++, UE4, AI developer
Genetic algorithm and Backpropagation in autonomous cars
Autonomous cars need learning in order to recognize their environment and behave consistently. Neural networks give us the opportunity to develop complex behaviors and tasks like driving.
I have implemented two algorithms that are widely used in machine learning: backpropagation (supervised learning) and genetic algorithm (reinforcement learning).
Autonomous car: learning and testing process.
Pages: 25
Author: Daniel López Montero
AI is a new field that appeared a few decades ago and has seen unprecedented growth. The AI approach has been used in numerous fields, such as finance, medicine, music, customer service, and transportation. Some of the big challenges we have to cope with are the computing power and lack of documentation. Big companies such as Google and Apple do not share advances in this field, the secretism characterizes this scientific field and thus the information we can get is very restrictive. In this work, we introduce a model of an autonomous car and then examine different algorithms capable of driving the car in a simulated. We also discuss the difficulties we need to deal with such as local minimums, losing diversity, fitness functions, localization, performance, and future applications.
Artificial intelligence, neural network, genetic algorithm, backpropagation, self-driving car.
Evolutionary algorithm learning process of cars in UE4 C++
AI is a new field that appeared a few decades ago and has seen an unprecedented growth. The AI approach has been used in numerous fields, such as finance, medicine, music, customer service, and transportation. Some of the big challenges we have to cope with are the computing power and lack of documentation. Big companies such as Google and Apple do not share advances in this field, the secretism characterizes this scientific field and thus the information we can get is very restrictive.
In this work, we introduce a model of an autonomous car and then examine different algorithms capable of driving the car in a simulated. We also discuss the difficulties we need to deal with such as local minimums, losing diversity, fitness functions, localization, performance, and future applications.
Genetic algorithm implementation in neural network
Genetic algorithms (GAs) are a heuristic search and optimisation technique inspired by natural evolution. They have been successfully applied to a wide range of real-world problems of significant complexity.
It is an algorithm that was inspired by the theory of evolution by Charles Darwin. It simulates the process of natural selection where the fittest individuals have higher probabilities to transfer their genes to the next generation. It is usually divided into 5 parts:
