While reinforcement learning has made great improvements, state-of-the-art algorithms can still struggle with seemingly simple set-point feedback control problems. One reason for this is that the learned controller may not be able to excite the system dynamics well enough initially, and therefore it can take a long time to get data that is informative enough to learn for good control. The paper contributes by augmentation of reinforcement learning with a simple guiding feedback controller, for example, a proportional controller. The key advantage in set point control is a much improved excitation that improves the convergence properties of the reinforcement learning controller significantly. This can be very important in real-world control where quick and accurate convergence is needed. The proposed method is evaluated with simulation and on a real-world double tank process with promising results.

翻译：尽管强化学习取得了很大的进步，但是现有的算法在看似简单的设定点反馈控制问题上仍然存在问题。其中一个原因是学习到的控制器可能无法足够激发系统动力学，从而需要长时间获取具有信息性的数据以学习良好的控制。本文提出了一种加强强化学习的方法，即增加一个简单的引导反馈控制器，例如比例控制器。在设定点控制方面，其优势在于大大改善了激励，显著提高了强化学习控制器的收敛性能。这在需要快速和准确收敛的实际控制中非常重要。所提出的方法通过仿真和实际的双鼓筒过程进行了评估，效果有望。