Reinforcement Learning for Linear Continuous-time Systems: an Incremental Learning Approach

Tao Bian

doi:10.1109/JAS.2019.1911390

Reinforcement Learning for Linear Continuous-time Systems: an Incremental Learning Approach

2019-03-01

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DOI：10.1109/JAS.2019.1911390

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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.

Keywords:

Adaptive optimal control

robust dynamic programming

value iteration (VI)

Reinforcement Learning for Linear Continuous-time Systems: an Incremental Learning Approach

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