Integrating Reinforcement Learning into Pipe System Design

The purpose of this paper is to describe one step in the broader research aimed at the design of machines using modern methods of artificial intelligence. This article explores the integration of reinforcement learning into pipe system design. The goal is to partially substitute the work of human designers. While integrating reinforcement learning into pipe system design may help streamline the process, it is important to consider the limitations and potential biases that could arise from relying solely on artificial intelligence. Therefore, the program must be robust enough to handle a variety of scenarios that may occur during the design process. The first part introduces the topic and provides an overview of the current state-of-the-art techniques. Later, the basic functionality of the neural networks and, in particular, reinforcement learning algorithms is discussed. The paper then proceeds with the application of machine design and introduces the presented solution and its main parts. The paper then concludes with the advantages of this approach, a description of its functionality and its limits. The very last part presents the authors’ future plans and research.