  Deep reinforcement learning (DRL) has recently been used to perform efficient
resource allocation in wireless communications. In this paper, the
vulnerabilities of such DRL agents to adversarial attacks is studied. In
particular, we consider multiple DRL agents that perform both dynamic channel
access and power control in wireless interference channels. For these victim
DRL agents, we design a jammer, which is also a DRL agent. We propose an
adversarial jamming attack scheme that utilizes a listening phase and
significantly degrades the users' sum rate. Subsequently, we develop an
ensemble policy defense strategy against such a jamming attacker by reloading
models (saved during retraining) that have minimum transition correlation.
