Yunzhe Tian, Yike Li, Yingxiao Xiang, Wenjia Niu, Endong Tong, and Jiqiang Liu (Beijing Jiaotong University)

Robust reinforcement learning has been a challenging problem due to always unknown differences between real and training environment. Existing efforts approached the problem through performing random environmental perturbations in learning process. However, one can not guarantee perturbation is positive. Bad ones might bring failures to reinforcement learning. Therefore, in this paper, we propose to utilize GAN to dynamically generate progressive perturbations at each epoch and realize curricular policy learning. Demo we implemented in unmanned CarRacing game validates the effectiveness.

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Demo #9: Attacking Multi-Sensor Fusion based Localization in High-Level...

Junjie Shen, Jun Yeon Won, Zeyuan Chen and Qi Alfred Chen (UC Irvine)

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NetPlier: Probabilistic Network Protocol Reverse Engineering from Message Traces

Yapeng Ye (Purdue University), Zhuo Zhang (Purdue University), Fei Wang (Purdue University), Xiangyu Zhang (Purdue University), Dongyan Xu (Purdue University)

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All the Numbers are US: Large-scale Abuse of Contact...

Christoph Hagen (University of Würzburg), Christian Weinert (TU Darmstadt), Christoph Sendner (University of Würzburg), Alexandra Dmitrienko (University of Würzburg), Thomas Schneider (TU Darmstadt)

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Oblivious DNS over HTTPS (ODoH): A Practical Privacy Enhancement...

Sudheesh Singanamalla*†, Suphanat Chunhapanya*, Jonathan Hoyland*, Marek Vavruša*, Tanya Verma*, Peter Wu*, Marwan Fayed*, Kurtis Heimerl†, Nick Sullivan*, Christopher Wood* (*Cloudflare Inc. †University of Washington)

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