This paper is a study of reinforcement learning (RL) as an optimal-control strategy for control of nonlinear valves. It is evaluated against the PID (proportional-integral-derivative) strategy, using a unified framework. RL is an autonomous learning mechanism that learns by interacting with its environment. It is gaining increasing attention in the world of control systems as a means of building optimal-controllers for challenging dynamic and nonlinear processes. Published RL research often uses open-source tools (Python and OpenAI Gym environments). We use MATLAB's recently launched (R2019a) Reinforcement Learning Toolbox to develop the valve controller; trained using the DDPG (Deep Deterministic Policy-Gradient) algorithm and Simulink to simulate the nonlinear valve and create the experimental test-bench for evaluation. Simulink allows industrial engineers to quickly adapt and experiment with other systems of their choice. Results indicate that the RL controller is extremely good at tracking the signal with speed and produces a lower error with respect to the reference signal. The PID, however, is better at disturbance rejection and hence provides a longer life for the valves. Successful machine learning involves tuning many hyperparameters requiring significant investment of time and efforts. We introduce "Graded Learning" as a simplified, application oriented adaptation of the more formal and algorithmic "Curriculum for Reinforcement Learning". It is shown via experiments that it helps converge the learning task of complex non-linear real world systems. Finally, experiential learnings gained from this research are corroborated against published research.

翻译：本文是强化学习(RL)的研究,是控制非线性阀门的最佳控制战略。我们使用MATLAB最近推出的(R2019a) 强化学习工具箱来开发阀门控制器;使用DDPG(深度确定性政策放大性)算法和Simmlink来模拟非线性阀门和创建实验测试箱以供评价,在控制系统的世界中日益受到重视,这是为挑战动态和非线性进程建立最佳控制器的一种手段。出版的RL研究经常使用开放源工具(Python和OpenAI Gym 环境)。我们使用MATLAB最近推出的(R2019a) 强化学习工具箱来开发阀门控制器;用DDPG(深确定性政策放大性)算法和Simmlinkink来培训,以模拟非线性阀门阀门阀和试验箱,使工业工程师能够迅速适应和试验他们选择的其他系统。结果显示,RL控制器在跟踪信号方面非常优秀,并在参考信号方面产生更低的错误。 PILElein Reflial Stal Stal Stalalisalisal real real real redudustrisdududustration lestration lestrutal dislation lading ahestrutal dism s