Simulation of the Navigation of a Mobile Robot by the QLearning using Artificial Neuron Networks.
Hatem Mezaache, Foudil Abdessemed · 2009
This paper presents a type of machine learning is reinforcement learning, this approach is often used in the field of robotics. It aims to determine a control law for a mobile robot in an unknown environment. This kind of technique applies when one assumes that the only information on the quality of actions performed by the mobile robot is a scalar signal which has a reward or punishment, the process of learning is to improve the choice of actions to maximize rewards. One of the most used algorithms for solving this problem is learning the Q-learning algorithm which is based on the Q-function, and to ensure the generation of this latter function and the proper functioning of the apprenticeship system using an artificial neural network as the statements of changing environments where mobile robots have wide open spaces, the action performed by the mobile robot in its environment is ensured by using a selection function, this action is evaluated by a scalar signal which is-1, 0 and 1.