Scalable Planning and Learning Framework Development for Swarm-to-Swarm Engagement Problems

TL;DR

This paper introduces a scalable reinforcement learning framework for planning and controlling large-scale swarm-to-swarm engagement scenarios, addressing a gap in existing methods that struggle with scalability.

Contribution

It proposes a novel RL-based approach that decomposes large-scale swarm engagement problems into multiple pursuit-evasion games for efficient planning.

Findings

Successful simulation of large-scale swarm engagement scenarios.

Finite time capture guarantees under certain conditions.

Effective high-level allocation of swarm units demonstrated.

Abstract

Development of guidance, navigation and control frameworks/algorithms for swarms attracted significant attention in recent years. That being said, algorithms for planning swarm allocations/trajectories for engaging with enemy swarms is largely an understudied problem. Although small-scale scenarios can be addressed with tools from differential game theory, existing approaches fail to scale for large-scale multi-agent pursuit evasion (PE) scenarios. In this work, we propose a reinforcement learning (RL) based framework to decompose to large-scale swarm engagement problems into a number of independent multi-agent pursuit-evasion games. We simulate a variety of multi-agent PE scenarios, where finite time capture is guaranteed under certain conditions. The calculated PE statistics are provided as a reward signal to the high level allocation layer, which uses an RL algorithm to allocate controlled swarm units to eliminate enemy swarm units with maximum efficiency. We verify our approach in large-scale swarm-to-swarm engagement simulations.