Web: http://arxiv.org/abs/2201.09149

Jan. 28, 2022, 2:11 a.m. | Juncheng Dong, Suya Wu, Mohammadreza Sultani, Vahid Tarokh

cs.LG updates on arXiv.org arxiv.org

Recently Reinforcement Learning (RL) has been applied as an anti-adversarial
remedy in wireless communication networks. However, studying the RL-based
approaches from the adversary's perspective has received little attention.
Additionally, RL-based approaches in an anti-adversary or adversarial paradigm
mostly consider single-channel communication (either channel selection or
single channel power control), while multi-channel communication is more common
in practice. In this paper, we propose a multi-agent adversary system (MAAS)
for modeling and analyzing adversaries in a wireless communication scenario by
careful design …

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