控制理论与应用2012，Vol.29Issue(12)：1623-1628,6.
移动机器人路径规划强化学习的初始化
Initialization in reinforcement learning for mobile robots path planning
摘要
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 proposed algorithm is validated by the robot path in the grid workspace. Results of experiments show that the improved algorithm promotes the learning efficiency in the early stage of learning, and improves the performance.关键词
移动机器人/强化学习/人工势能场/路径规划/Q值初始化Key words
mobile robots/ reinforcement learning/ artificial potential field/ path planning/ Q values initialization分类
信息技术与安全科学引用本文复制引用
宋勇,李贻斌,李彩虹..移动机器人路径规划强化学习的初始化[J].控制理论与应用,2012,29(12):1623-1628,6.基金项目
国家自然科学基金资助项目(61075091,61174054) （61075091,61174054）
国家自然科学基金青年基金资助项目(61105100). （61105100）
