  In this paper, genetic programming reinforcement learning (GPRL) is utilized
to generate human-interpretable control policies for a Chylla-Haase
polymerization reactor. Such continuously stirred tank reactors (CSTRs) with
jacket cooling are widely used in the chemical industry, in the production of
fine chemicals, pigments, polymers, and medical products. Despite appearing
rather simple, controlling CSTRs in real-world applications is quite a
challenging problem to tackle. GPRL utilizes already existing data from the
reactor and generates fully automatically a set of optimized simplistic control
strategies, so-called policies, the domain expert can choose from. Note that
these policies are white-box models of low complexity, which makes them easy to
validate and implement in the target control system, e.g., SIMATIC PCS 7.
However, despite its low complexity the automatically-generated policy yields a
high performance in terms of reactor temperature control deviation, which we
empirically evaluate on the original reactor template.
