Reinforcement learning for linear continuous-time systems: an incremental learning approach

In this paper, we introduce a novel reinforcement learning ( RL ) scheme for linear continuous-time dynamical systems. Different from traditional batch learning algorithms, an incremental learning approach is developed, which provides a more efficient way to tackle the on-line learning problem in real-world applications. We provide concrete convergence and robust analysis on this incremental-learning algorithm. An extension to solving robust optimal control problems is also given. Two simulation examples are also given to illustrate the effectiveness of our theoretical result.