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.
翻译:在这项工作中,我们提议了一个强化学习框架,以控制大规模群集的密度,用于应对对抗性群发攻击。虽然在应用人工智能方法进行群发控制方面现有大量工作,但分析两个对立群之间的互动是一个相当不足的研究领域。本主题的现有工作大多通过对对敌对群发的战略和动态作出硬性假设来制定战略。我们的主要贡献是将群集形成群聚参与问题,作为马尔科夫决策程序,并发展RL算法,这种算法可以在对敌对群发战略/动力不知情的情况下计算参与战略。模拟结果显示,发达的框架能够有效地处理范围广泛的大规模参与情景。