Adversarial imitation learning with deep attention network for swarm systems

IF 5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yapei Wu, Tao Wang, Tong Liu, Zhicheng Zheng, Demin Xu, Xingguang Peng
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引用次数: 0

Abstract

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.

利用深度注意力网络为蜂群系统提供对抗性模仿学习
蜂群系统由大量相互影响的个体组成,尽管交互规则简单,但却表现出复杂的行为。然而,由于个体策略与蜂群动力学之间的关系错综复杂,因此制定能够体现理想集体行为的个体运动策略是一项重大挑战。本文通过提出一种模仿学习方法来解决这一问题,该方法可从集体行为数据中推导出个体策略。该方法利用对抗式模仿学习框架,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分析便利,我们验证了某种集体行为所依据的个体策略并不是唯一的。此外,我们还分析了所发现的不同个体策略。最后,我们通过在蜂群机器人上的实际应用,验证了所提出的方法在为蜂群机器人设计策略时的适用性。
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来源期刊
Complex & Intelligent Systems
Complex & Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
9.60
自引率
10.30%
发文量
297
期刊介绍: Complex & Intelligent Systems aims to provide a forum for presenting and discussing novel approaches, tools and techniques meant for attaining a cross-fertilization between the broad fields of complex systems, computational simulation, and intelligent analytics and visualization. The transdisciplinary research that the journal focuses on will expand the boundaries of our understanding by investigating the principles and processes that underlie many of the most profound problems facing society today.
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