标题： 基于强化学习的随机系统的平均成本最优控制 显示英文标题

标题： Average Cost Optimal Control of Stochastic Systems Using Reinforcement Learning

摘要： 本文研究了具有乘性和加性噪声的离散时间系统的平均成本最小化问题，通过强化学习方法。 通过使用Q函数，我们提出了一种在线学习方案来估计Q函数的核矩阵，并利用系统轨迹上的数据更新控制增益。 获得的控制增益和核矩阵被证明会收敛到最优值。 为了实现所提出的学习方案，给出了一种在线无模型强化学习算法，其中使用递归最小二乘法来估计Q函数的核矩阵。 给出了一个数值示例来说明所提出的方法。 显示英文摘要

摘要： This paper addresses the average cost minimization problem for discrete-time systems with multiplicative and additive noises via reinforcement learning. By using Q-function, we propose an online learning scheme to estimate the kernel matrix of Q-function and to update the control gain using the data along the system trajectories. The obtained control gain and kernel matrix are proved to converge to the optimal ones. To implement the proposed learning scheme, an online model-free reinforcement learning algorithm is given, where recursive least squares method is used to estimate the kernel matrix of Q-function. A numerical example is presented to illustrate the proposed approach.