关键词: 移动机器人, Q学习, 支持向量机, 导航, 在线学习

Abstract: Continuous Q-learning algorithm based on neural has been used in robotic navigation domain for its simplicity and well-developed theory.Aiming at the neural easily falling into local minimum，a new mobile robot navigation method using Q-learning based on a Support Vector Machine（SVM） is proposed.According to the developed mobile robot CASIA-I and its working environment，an approach is proposed，used to determine the reward/penalty function of Q-learning.A SVM is used to estimate the Q-value of state-action pair on-line，at the same time，in order to decrease the on-time learning time of SVM，a sliding time-window is introduced.Experimental results are included to show that the action policy obtained through Q-learning based on SVM can make the mobile robot reach the destination without obstacle collision.

Key words: mobile robot, Q-learning, support vector machine, navigation, on-line learning