  Deep reinforcement learning (RL) is an optimization-driven framework for
producing control strategies for general dynamical systems without explicit
reliance on process models. Good results have been reported in simulation. Here
we demonstrate the challenges in implementing a state of the art deep RL
algorithm on a real physical system. Aspects include the interplay between
software and existing hardware; experiment design and sample efficiency;
training subject to input constraints; and interpretability of the algorithm
and control law. At the core of our approach is the use of a PID controller as
the trainable RL policy. In addition to its simplicity, this approach has
several appealing features: No additional hardware needs to be added to the
control system, since a PID controller can easily be implemented through a
standard programmable logic controller; the control law can easily be
initialized in a "safe'' region of the parameter space; and the final product
-- a well-tuned PID controller -- has a form that practitioners can reason
about and deploy with confidence.
