Abstract:
An approach to the creation of intelligent object control systems using machine learning with reinforcement is
illustrated using the example of an intersection control system. The simulation model of the intersection, chosen as the learning
environment, is described. The results of a comparative analysis of the performance of various learning algorithms are
presented. The results of applying the Monte Carlo policy gradient to train the intersection model are presented.