# Transparent Machine Education of Neural Networks for Swarm Shepherding Using Curriculum Design

## TL;DR

This paper introduces a curriculum-based approach to train neural networks for swarm shepherding, improving learning efficiency and behavioral complexity in dynamic environments.

## Contribution

It presents a novel curriculum design method that enables transparent and efficient training of AI agents for complex swarm control tasks.

## Key findings

- Curriculum design accelerates learning speed.
- Enhanced behavioral complexity in trained agents.
- Improved performance in dynamic swarm environments.

## Abstract

Swarm control is a difficult problem due to the need to guide a large number of agents simultaneously. We cast the problem as a shepherding problem, similar to biological dogs guiding a group of sheep towards a goal. The shepherd needs to deal with complex and dynamic environments and make decisions in order to direct the swarm from one location to another. In this paper, we design a novel curriculum to teach an artificial intelligence empowered agent to shepherd in the presence of the large state space associated with the shepherding problem and in a transparent manner. The results show that a properly designed curriculum could indeed enhance the speed of learning and the complexity of learnt behaviours.