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.

翻译：强化学习作为一种强大的机器学习范式，已在机器人技术、自然语言处理和游戏博弈等多个领域取得最先进成果，其专为求解序列决策问题而设计，天生具备从经验中学习并适应动态变化环境的能力。这些特性使其成为工业复杂过程控制与优化的理想候选方案。充分释放这一潜力的关键在于将强化学习无缝集成到现有工业系统中。工业通信标准OPC统一架构（OPC UA）有望弥合这一鸿沟。然而，由于强化学习与OPC UA分属不同领域，研究者亟需搭建二者技术融合的桥梁。本文通过概述两种技术的基本原理，并开展半详尽的文献综述以探析强化学习与OPC UA的协同应用路径，旨在填补上述研究空白。基于本次综述，我们识别出强化学习与OPC UA交叉领域的三大核心研究方向。文献调研结果表明：强化学习在工业过程控制与优化领域具有广阔应用前景，但目前仍缺乏标准化接口，难以在真实场景中以较低投入实现部署。