An Embedded Feature Selection Framework for Control

Abstract

Reducing sensor requirements while keeping optimal control performance is crucial to many industrial control applications to achieve robust, low-cost, and computation-efficient controllers. However, existing feature selection solutions for the typical machine learning domain can hardly be applied in the domain of control with changing dynamics. In this paper, a novel framework, namely the Dual-world embedded Attentive Feature Selection (D-AFS), can efficiently select the most relevant sensors for the system under dynamic control. Rather than the one world used in most Deep Reinforcement Learning (DRL) algorithms, D-AFS has both the real world and its virtual peer with twisted features. By analyzing the DRL's response in two worlds, D-AFS can quantitatively identify respective features' importance towards control. A well-known active flow control problem, cylinder drag reduction, is used for evaluation. Results show that D-AFS successfully finds an optimized five-probes layout with 18.7\% drag reduction than the state-of-the-art solution with 151 probes and 49.2\% reduction than five-probes layout by human experts. We also apply this solution to four OpenAI classical control cases. In all cases, D-AFS achieves the same or better sensor configurations than originally provided solutions. Results highlight, we argued, a new way to achieve efficient and optimal sensor designs for experimental or industrial systems. Our source codes are made publicly available at https://github.com/G-AILab/DAFSFluid.

arXiv comment9 pages, 9 figures, accepted by SIGKDD 2022

References in corpus (9)

• Scikit-learn: Machine Learning in Python
• XGBoost: A Scalable Tree Boosting System
• Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
• OpenAI Gym
• Artificial Neural Networks trained through Deep Reinforcement Learning discover control strategies for active flow control
• Generalized Fisher Score for Feature Selection
• Accelerating Deep Reinforcement Learning strategies of Flow Control through a multi-environment approach
• Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning
• Feature Importance Ranking for Deep Learning