  Proportional-integral-derivative (PID) control underlies more than $97\%$ of
automated industrial processes. Controlling these processes effectively with
respect to some specified set of performance goals requires finding an optimal
set of PID parameters to moderate the PID loop. Tuning these parameters is a
long and exhaustive process. A method (patent pending) based on deep
reinforcement learning is presented that learns a relationship between generic
system properties (e.g. resonance frequency), a multi-objective performance
goal and optimal PID parameter values. Performance is demonstrated in the
context of a real optical switching product of the foremost manufacturer of
such devices globally. Switching is handled by piezoelectric actuators where
switching time and optical loss are derived from the speed and stability of
actuator-control processes respectively. The method achieves a $5\times$
improvement in the number of actuators that fall within the most challenging
target switching speed, $\geq 20\%$ improvement in mean switching speed at the
same optical loss and $\geq 75\%$ reduction in performance inconsistency when
temperature varies between 5 and 73 degrees celcius. Furthermore, once trained
(which takes $\mathcal{O}(hours)$), the model generates actuator-unique PID
parameters in a one-shot inference process that takes $\mathcal{O}(ms)$ in
comparison to up to $\mathcal{O}(week)$ required for conventional tuning
methods, therefore accomplishing these performance improvements whilst
achieving up to a $10^6\times$ speed-up. After training, the method can be
applied entirely offline, incurring effectively zero optimisation-overhead in
production.
