汽车工业
强化学习
控制(管理)
计算机科学
校准
钢筋
工程类
人工智能
数学
统计
结构工程
航空航天工程
作者
Thomas Rudolf,Tobias Schürmann,Matteo Skull,Stefan Schwab,Sören Hohmann
出处
期刊:Proceedings
日期:2022-01-01
卷期号:: 369-384
被引量:1
标识
DOI:10.1007/978-3-658-37009-1_26
摘要
The importance of data-driven methods in automotive development continuously increases. In this area, reinforcement learning methods show great potential, but the required data from system interaction can be expensive to produce during the traditional development process. In the automotive industry, data collection is additionally constrained by privacy aspects with regard to intellectual property interests or customer data. Suitable reinforcement learning approaches need to overcome these challenges for effective and efficient learning. One possible solution is the utilization of federated learning that enables learning on distributed data through model aggregation. Therefore, we investigate the federated reinforcement learning methodology and propose a concept for a continuous automotive development process. The concept contributes separated training loops for the development and for the field operation. Furthermore, we present a customization and verification procedure within the aggregation step. The approach is exemplary shown for an electric motor current control.
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