移动机器人路径规划强化学习的初始化-控制理论与应用.pdf

29 12 Vol. 29 No. 12 2012 12 Control Theory Applications Dec. 2012 :2012 , , (1. , 250061; 2. () , 264209; 3. , 255012) : , . , , . , . . , , . , , . : ; ; ; ; : TP242 : A Initialization in reinforcement learning for mobile robots path planning SONG Yong , LI Yi-bin , LI Cai-hong (1. School of Control Science and Engineering, Shandong University, Jinan Shandong 250061, China; 2. School of Mechanical, Electrical Information Engineering, Shandong University at Weihai, Weihai Shandong 264209, China; 3. School of Computer Science and Technology, Shandong University of Technology, Zibo Shandong 255012, China ) Abstract: To improve the convergence rate of the standard Q-learning algorithm, we propose an initialization method for the reinforcement learning of the mobile robot, based on the artificial potential field (APF) -a virtue field of the robot workspace. The potential energy of each point in the field is specified based on prior knowledge, which represents the maximum cumulative reward by following the optimal path policy. In APF, points corresponding to obstacles have null potential energy; the objective point has the global maximum potential energy in the workspace. The initial Q value is defined as the immediate reward at the current point plus the maximum cumulative reward at succeeding points by following the optimal path policy. By initializing the Q value, we find that the improved algorithm converges more rapidly and steadily than the original algorithm. The propose