论文标题
通过加强学习方法进行多机构协调的战略操纵和破坏
Strategic Maneuver and Disruption with Reinforcement Learning Approaches for Multi-Agent Coordination
论文作者
论文摘要
加强学习（RL）方法可以照亮新兴行为，这些行为促进了代理团队的协调，作为多机构系统（MAS）的一部分，可以在各种军事任务中提供机会窗口。从技术上讲，对手在友好国家的利益和资源上构成了重大风险。仅凭优越的资源就不足以在现代复杂环境中击败对手，因为对手在多个领域造成了对抗可预测的基于军事学说的动作的僵持。因此，作为防御策略的一部分，友好的力量必须使用战略操纵和破坏，以在复杂的多方面域（例如多域操作（MDO））中获得优越性。实施战略动作和中断以获得优越性比对手的一种有希望的途径是通过在未来的军事行动中协调MAS。在本文中，我们概述了RL领域中著名作品的概述，其优点和缺点克服了与在军事环境中进行自主战略动作和破坏相关的挑战。
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.
