Reinforcement Learning for Engineering Design Automation. (April 2022)

• Record Type: Journal Article
• Authors: Dworschak, Fabian; Dietze, Sebastian; Wittmann, Maximilian; Schleich, Benjamin; Wartzack, Sandro
• Is Part Of: Advanced engineering informatics. Volume 52(2022)
• Publication Date: 2022-04
• Subjects: Reinforcement Learning -- Design Automation -- Deep Q-learning; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2022.101612
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: Reinforcement Learning has proven to be capable of solving complex tasks like playing video games, robotics control, speech or image recognition and processing. Transferring Reinforcement Learning into engineering design helps to overcome two current issues of data-driven Design Automation in engineering design. First, dealing with sparse training data resulting from differing design samples. Second, overcoming the limited number of samples in the training data as consequence of short or insufficient product history. To introduce an alternative approach for Design Automation, this contribution studies feasibility, training effort and transferability of Reinforcement Learning in engineering design. The presented method maps engineering requirements and parametric models into learning environments and provides a novel approach for design automation. In addition to that, the contribution summarises the hyperparameters, which design engineers have to set prior to training, and introduces a novel transfer learning concept for Reinforcement Learning in related design tasks. The support is probed by design tasks of performance-oriented bike parts. Case-independent indicators are presented to estimate the case-specific training effort, the effects of hyperparameter variation and the effects of transferring a pretrained agent to related design tasks. Finally, the findings are used to compare Reinforcement Learning to other data-independent Design Automation approaches to assess potential fields of application for Reinforcement Learning in engineering design. Highlights: Method for Reinforcement Learning as an alternative Design Automation approach. Parametric CAD models as learning environments and requirements as reward functions. Quantitative Indicators for training effort estimation to enable comparability. Studies on feasibility, training effort and effects of pre-training. Identification of potential user groups for Reinforcement Learning.