清华首次提出数据驱动控制新形式，算法效率直翻三倍

Core Viewpoint - The article discusses a paradigm shift in control theory from model-driven control to data-driven control, emphasizing the need for a standardized data representation in the latter to enhance efficiency and reduce redundancy in algorithm design [1][7][12]. Group 1: Introduction to Data-Driven Control - The rise of big data has led to a new turning point in control theory, transitioning from reliance on models to reliance on data [1]. - There is a lack of standardized data representation in the data-driven control field, prompting research from Tsinghua University to introduce a new data-based system description format [2][12]. Group 2: Standard Form in Data-Driven Control - Each sample in the proposed standard form consists of necessary transfer and pluggable attributes, which describe system dynamics and user-defined features, respectively [3][19]. - The new data standard form can be customized according to algorithm requirements, significantly accelerating controller design and improving the efficiency of data-driven algorithms [4][32]. Group 3: Challenges and Solutions - The transition to data-driven control faces challenges due to the vast amounts of complex interaction data generated by systems like robotics and autonomous driving [12]. - Efficiently organizing and describing data to minimize redundant calculations and speed up algorithm execution is a core challenge in data-driven control [16]. Group 4: Application of Data Standard Form - A typical application example demonstrates that many reinforcement learning algorithms require nearest neighbor searches to ensure reliable controller design [20]. - By pre-defining spatial attributes for each sample, the proposed data standard form can significantly accelerate the nearest neighbor search process [22][28]. Group 5: Experimental Results - Experiments conducted in the D4RL dataset's Hopper environment showed that using the spatial standard form reduced training time from approximately 20 hours to just 7 hours, achieving a threefold efficiency improvement [29][31].