  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.
