中文摘要：

提出一种基于增强学习的双轮驱动移动机器人路径跟随控制方法，通过将机器人运动控制器的优化设计问题建模为Markov决策过程，采用基于核的最小二乘策略迭代算法（KLSPI）实现控制器参数的自学习优化。与传统表格型和基于神经网络的增强学习方法不同，KLSPI算法在策略评价中应用核方法进行特征选择和值函数逼近，从而提高了泛化性能和学习效率。仿真结果表明，该方法通过较少次数的迭代就可以获得优化的路径跟随控制策略，有利于在实际应用中的推广。

英文摘要：

This paper proposed a novel self-learning path-following control method based on reinforcement learning for a class of two-wheeled mobile robots. The path-following control problem of autonomous vehicles was modelled as a Markov decision process （MDP） and by using the kernel least-squares policy iteration （KLSPI） algorithm, the lateral control performance of the two-wheeled mobile robot could be optimized in a self-learning style. Unlike traditional table-based reinforcement learning （RL） and RL based on neural networks, KLSPI used kernel methods with automatic feature selection and value function approximation in policy evaluation so that better generalization performance and learning efficiency could be obtained. Simulation results show that the proposed method can obtain an optimized path-following control policy only in a few iterations, which will be very practical for real applications.