  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.
