Swarm systems consist of a large number of interacting individuals, which exhibit complex behavior despite having simple interaction rules. However, crafting individual motion policies that can manifest desired collective behaviors poses a significant challenge due to the intricate relationship between individual policies and swarm dynamics. This paper addresses this issue by proposing an imitation learning method, which derives individual policies from collective behavior data. The approach leverages an adversarial imitation learning framework, with a deep attention network serving as the individual policy network. Our method successfully imitates three distinct collective behaviors. Utilizing the ease of analysis provided by the deep attention network, we have verified that the individual policies underlying a certain collective behavior are not unique. Additionally, we have analyzed the different individual policies discovered. Lastly, we validate the applicability of the proposed method in designing policies for swarm robots through practical implementation on swarm robots.

Swarm 系统由大量交互个体组成，尽管交互规则简单，但这些个体表现出复杂的行为。然而，由于单个策略和群体动态之间的复杂关系，制定能够体现所需集体行为的单个运动策略构成了重大挑战。本文通过提出一种模仿学习方法来解决这个问题，该方法从集体行为数据中得出单个策略。该方法利用对抗性模仿学习框架，以深度注意力网络作为个体策略网络。我们的方法成功地模仿了三种不同的集体行为。利用深度注意力网络提供的易分析性，我们已经验证了某种集体行为背后的个体政策并不是唯一的。此外，我们还分析了发现的不同单个策略。最后，通过在群体机器人上的实际实施，验证了所提方法在群体机器人策略设计中的适用性。