  Microgrids (MGs) are small-scale power systems which interconnect distributed
energy resources and loads within clearly defined regions. However, the digital
infrastructure used in an MG to relay sensory information and perform control
commands can potentially be compromised due to a cyberattack from a capable
adversary. An MG operator is interested in knowing the inherent vulnerabilities
in their system and should regularly perform Penetration Testing (PT)
activities to prepare for such an event. PT generally involves looking for
defensive coverage blindspots in software and hardware infrastructure, however
the logic in control algorithms which act upon sensory information should also
be considered in PT activities. This paper demonstrates a case study of PT for
an MG control algorithm by using Reinforcement Learning (RL) to uncover
malicious input which compromises the effectiveness of the controller. Through
trial-and-error episodic interactions with a simulated MG, we train an RL agent
to find malicious input which reduces the effectiveness of the MG controller.
