Application and comparison of evolutionary algorithms in the framework of the problem of reinforcement learning for unstable systems
Abstract
Application and comparison of evolutionary algorithms in the framework of the problem of reinforcement learning for unstable systems
Incoming article date: 17.04.2023The aim of this work is the implementation and comparison of genetic algorithms in the framework of the problem of reinforcement learning for the control of unstable systems. The unstable system will be the CartPole Open AI GYM object, which simulates the balancing of a rod hinged on a cart that moves left and right. The goal is to keep the pole in a vertical position for as long as possible. The control of this object is implemented using two learning methods: the neuroevolutionary algorithm (NEAT) and the multilayer perceptron using genetic algorithms (DEAP).
Keywords: machine learning, non-revolutionary algorithms, genetic algorithms, reinforcement learning, neural networks