A Navigation Algorithm Based on the Reinforcement Learning Reward System and Optimised with Genetic Algorithm

• Published in: Mathematics (Basel) 2024-12, Vol.12 (24), p.4030
• Main Authors: Cabezas-Olivenza, Mireya, Zulueta, Ekaitz, Azurmendi-Marquinez, Iker, Fernandez-Gamiz, Unai, Rico-Melgosa, Danel
• Format: Article
• Language: English
• Subjects: Algorithms; Autonomous navigation; autonomous vehicle; Convergence; Driverless cars; genetic algorithm; Genetic algorithms; Machine learning; navigation; Neural networks; Obstacle avoidance; optimisation; Parameters; Planning; q-learning; reinforcement learning; Robots; Vehicles
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math12244030

Regarding autonomous vehicle navigation, reinforcement learning is a technique that has demonstrated significant results. Nevertheless, it is a technique with a high number of parameters that need to be optimised without prior information, and correctly performing this is a complicated task. In this research study, a system based on the principles of reinforcement learning, specifically on the concept of rewards, is presented. A mathematical expression was proposed to control the vehicle’s direction based on its position, the obstacles in the environment and the destination. In this equation proposal, there was only one unknown parameter that regulated the degree of the action to be taken, and this was optimised through the genetic algorithm. In this way, a less computationally expensive navigation algorithm was presented, as it avoided the use of neural networks. The controller’s time to obtain the navigation instructions was around 6.201·10−4 s. This algorithm is an efficient and accurate system which manages not to collide with obstacles and to reach the destination from any position. Moreover, in most cases, it has been found that the proposed navigations are also optimal.