论文标题
一种可扩展的加强学习方法，用于群体中的攻击分配以群的参与问题
A Scalable Reinforcement Learning Approach for Attack Allocation in Swarm to Swarm Engagement Problems
论文作者
论文摘要
在这项工作中，我们提出了一个增强学习（RL）框架，该框架控制着大规模群的密度，以参与对抗性群体攻击。尽管将人工智能方法应用于群体控制时有大量现有工作，但分析两个对抗性群之间的相互作用是一个相当研究的领域。该主题中的大多数现有工作通过对对抗性群的策略和动态做出艰难的假设来制定策略。我们的主要贡献是将群体的群体群体制定为马尔可夫决策过程和RL算法的开发，这些算法可以计算参与策略，而无需了解对抗性群的策略/动态。仿真结果表明，开发的框架可以有效地处理各种大规模参与方案。
In this work we propose a reinforcement learning (RL) framework that controls the density of a large-scale swarm for engaging with adversarial swarm attacks. Although there is a significant amount of existing work in applying artificial intelligence methods to swarm control, analysis of interactions between two adversarial swarms is a rather understudied area. Most of the existing work in this subject develop strategies by making hard assumptions regarding the strategy and dynamics of the adversarial swarm. Our main contribution is the formulation of the swarm to swarm engagement problem as a Markov Decision Process and development of RL algorithms that can compute engagement strategies without the knowledge of strategy/dynamics of the adversarial swarm. Simulation results show that the developed framework can handle a wide array of large-scale engagement scenarios in an efficient manner.
