# Emergence of Longitudinal Queues in Group Navigation: An Interpretable Approach via Projective Simulation

## TL;DR

This paper introduces a transparent model for how groups form efficient navigation queues, revealing a target-priority mechanism that helps maintain order.

## Contribution

The study introduces an interpretable model using Projective Simulation and Episodic Compositional Memory to explain queue formation in swarms.

## Key findings

- Swarm systems self-organize into stable longitudinal queues using a target-priority mechanism.
- Moderate control precision in action space optimizes queue stability.
- The model reveals how individuals prioritize global targets over local alignment.

## Abstract

The formation of longitudinal queues is critical for biological and artificial swarm systems to achieve efficient long-distance navigation. However, the “black-box” nature of conventional deep reinforcement learning models often obscures the microscopic rules driving the emergence of such ordered behaviors. To address this challenge, this paper proposes an interpretable computational model of collective behavior based on Projective Simulation and Episodic Compositional Memory, which enables individuals to learn decision-making strategies within a transparent state–action network. Simulation results demonstrate that the swarm can self-organize into stable and highly elongated longitudinal queues. Crucially, through visualization of microscopic strategies, we reveal a deterministic target-priority mechanism: when local neighbor alignment conflicts with global target orientation, individuals learn to strictly prioritize the target direction, serving as the key driving force for queue formation. Further parametric analysis indicates that the action space granularity exerts a nonlinear impact on stability, identifying moderate control precision as the optimal choice. This study not only provides a transparent computational explanation for the self-organization mechanism of queues in collective motion but also offers a theoretical foundation for designing interpretable swarm navigation systems.