Strategic Maneuver and Disruption with Reinforcement Learning Approaches for Multi-Agent Coordination

TL;DR

This paper explores how reinforcement learning can enable multi-agent systems to perform strategic maneuvers and disruption in military operations, aiming to enhance coordination and superiority against technologically advanced adversaries.

Contribution

It provides an overview of RL methods for autonomous strategic maneuver and disruption in military multi-agent systems, highlighting strengths and weaknesses.

Findings

RL facilitates emergent coordinated behaviors in multi-agent systems

RL-based approaches can improve strategic disruption capabilities

The paper identifies challenges and future directions for RL in military contexts

Abstract

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.