  Reinforcement Learning (RL) is a powerful machine learning paradigm that has
been applied in various fields such as robotics, natural language processing
and game playing achieving state-of-the-art results. Targeted to solve
sequential decision making problems, it is by design able to learn from
experience and therefore adapt to changing dynamic environments. These
capabilities make it a prime candidate for controlling and optimizing complex
processes in industry. The key to fully exploiting this potential is the
seamless integration of RL into existing industrial systems. The industrial
communication standard Open Platform Communications UnifiedArchitecture (OPC
UA) could bridge this gap. However, since RL and OPC UA are from different
fields,there is a need for researchers to bridge the gap between the two
technologies. This work serves to bridge this gap by providing a brief
technical overview of both technologies and carrying out a semi-exhaustive
literature review to gain insights on how RL and OPC UA are applied in
combination. With this survey, three main research topics have been identified,
following the intersection of RL with OPC UA. The results of the literature
review show that RL is a promising technology for the control and optimization
of industrial processes, but does not yet have the necessary standardized
interfaces to be deployed in real-world scenarios with reasonably low effort.
