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：尽管强化学习已取得显著进展，最先进的算法仍可能在看似简单的设定点反馈控制问题中遇到困难。其原因之一是，初始阶段学习的控制器可能无法充分激发系统动态特性，因此需要长时间才能获取具有足够信息量的数据以实现良好控制。本文通过将强化学习与简单引导反馈控制器（例如比例控制器）相结合来做出贡献。在设定点控制中，关键优势在于激发作用显著增强，从而大幅改善强化学习控制器的收敛特性。这在需要快速准确收敛的实际控制场景中尤为重要。所提方法通过仿真实验及真实双水箱过程验证，取得了令人满意的结果。