A Review on NEAT and Other Reinforcement Algorithms in Robotics

Artificial neural networks, or ANNs, find widespread use in real-world scenarios ranging from pattern recognition to robotics control. Choosing an architecture (which includes a model for the neurons) and a learning algorithm are important decisions when building a neural network for a particular purpose. An automated method for resolving these issues is provided by evolutionary search techniques.Genetic algorithms are used to artificially evolve neural networks in a process known as neuroevolution (NE), which has shown great promise in solving challenging reinforcement learning problems. This study offers a thorough review of the state-of-the-art techniques for evolving artificial neural networks (ANNs), with a focus on optimizing their performance-enhancing capabilities.