A Scalable Reinforcement Learning Approach for Attack Allocation in Swarm to Swarm Engagement Problems

TL;DR

This paper introduces a scalable reinforcement learning framework for controlling large-scale swarms in adversarial engagement scenarios, enabling strategy computation without prior knowledge of the opponent's tactics.

Contribution

It formulates the swarm engagement problem as a Markov Decision Process and develops RL algorithms that operate without assumptions on adversary strategies or dynamics.

Findings

Framework effectively handles large-scale engagement scenarios

RL algorithms operate without prior knowledge of adversary tactics

Simulation results demonstrate efficiency and scalability

Abstract

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.