  Reinforcement learning (RL) approaches can illuminate emergent behaviors that
facilitate coordination across teams of agents as part of a multi-agent system
(MAS), which can provide windows of opportunity in various military tasks.
Technologically advancing adversaries pose substantial risks to a friendly
nation's interests and resources. Superior resources alone are not enough to
defeat adversaries in modern complex environments because adversaries create
standoff in multiple domains against predictable military doctrine-based
maneuvers. Therefore, as part of a defense strategy, friendly forces must use
strategic maneuvers and disruption to gain superiority in complex multi-faceted
domains such as multi-domain operations (MDO). One promising avenue for
implementing strategic maneuver and disruption to gain superiority over
adversaries is through coordination of MAS in future military operations. In
this paper, we present overviews of prominent works in the RL domain with their
strengths and weaknesses for overcoming the challenges associated with
performing autonomous strategic maneuver and disruption in military contexts.
