The impact of Q-learning parameters on robot path planning problems in different complex environment

Abstract

With the development of reinforcement learning algorithms, its efficient problem-solving model and the universality of the method have been favoured by many scholars. More and more robot path planning problems are solved using reinforcement learning methods. This article focuses on the Q-learning algorithm, uses MATLAB to study the impact of Q-learning parameters on robot path planning problems in different complex environments, and tries to find the optimal solution to the parameters. Research has found that environmental complexity has a significant impact on the speed at which robots solve path planning problems, and the optimal solutions to problem parameters in different environments require detailed analysis of specific problems.