  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 of the reward function under realistic communication scenarios.
In particular, by modeling the adversaries as learning agents, we show that the
proposed MAAS is able to successfully choose the transmitted channel(s) and
their respective allocated power(s) without any prior knowledge of the sender
strategy. Compared to the single-agent adversary (SAA), multi-agents in MAAS
can achieve significant reduction in signal-to-noise ratio (SINR) under the
same power constraints and partial observability, while providing improved
stability and a more efficient learning process. Moreover, through empirical
studies we show that the results in simulation are close to the ones in
communication in reality, a conclusion that is pivotal to the validity of
performance of agents evaluated in simulations.
