swarm：构建智能主动系统的未来

IF 1.8 4区物理与天体物理 Q4 CHEMISTRY, PHYSICAL

The European Physical Journal E Pub Date : 2025-04-07 DOI:10.1140/epje/s10189-025-00477-4

Samuel Tovey, Christoph Lohrmann, Tobias Merkt, David Zimmer, Konstantin Nikolaou, Simon Koppenhöfer, Anna Bushmakina, Jonas Scheunemann, Christian Holm

{"title":"swarm：构建智能主动系统的未来","authors":"Samuel Tovey, Christoph Lohrmann, Tobias Merkt, David Zimmer, Konstantin Nikolaou, Simon Koppenhöfer, Anna Bushmakina, Jonas Scheunemann, Christian Holm","doi":"10.1140/epje/s10189-025-00477-4","DOIUrl":null,"url":null,"abstract":"<div>This work introduces SwarmRL, a Python package designed to study intelligent active particles. SwarmRL provides an easy-to-use interface for developing models to control microscopic colloids using classical control and deep reinforcement learning approaches. These models may be deployed in simulations or real-world environments under a common framework. We explain the structure of the software and its key features and demonstrate how it can be used to accelerate research. With SwarmRL, we aim to streamline research into micro-robotic control while bridging the gap between experimental and simulation-driven sciences. SwarmRL is available open-source on GitHub at https://github.com/SwarmRL/SwarmRL.</div>","PeriodicalId":790,"journal":{"name":"The European Physical Journal E","volume":"48 4-5","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1140/epje/s10189-025-00477-4.pdf","citationCount":"0","resultStr":"{\"title\":\"SwarmRL: building the future of smart active systems\",\"authors\":\"Samuel Tovey, Christoph Lohrmann, Tobias Merkt, David Zimmer, Konstantin Nikolaou, Simon Koppenhöfer, Anna Bushmakina, Jonas Scheunemann, Christian Holm\",\"doi\":\"10.1140/epje/s10189-025-00477-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>This work introduces SwarmRL, a Python package designed to study intelligent active particles. SwarmRL provides an easy-to-use interface for developing models to control microscopic colloids using classical control and deep reinforcement learning approaches. These models may be deployed in simulations or real-world environments under a common framework. We explain the structure of the software and its key features and demonstrate how it can be used to accelerate research. With SwarmRL, we aim to streamline research into micro-robotic control while bridging the gap between experimental and simulation-driven sciences. SwarmRL is available open-source on GitHub at https://github.com/SwarmRL/SwarmRL.</div>\",\"PeriodicalId\":790,\"journal\":{\"name\":\"The European Physical Journal E\",\"volume\":\"48 4-5\",\"pages\":\"\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2025-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1140/epje/s10189-025-00477-4.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The European Physical Journal E\",\"FirstCategoryId\":\"4\",\"ListUrlMain\":\"https://link.springer.com/article/10.1140/epje/s10189-025-00477-4\",\"RegionNum\":4,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The European Physical Journal E","FirstCategoryId":"4","ListUrlMain":"https://link.springer.com/article/10.1140/epje/s10189-025-00477-4","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}

引用次数: 0

摘要

本文介绍了用于研究智能活性粒子的Python包SwarmRL。SwarmRL提供了一个易于使用的界面，用于开发使用经典控制和深度强化学习方法控制微观胶体的模型。这些模型可以部署在一个通用框架下的模拟或真实环境中。我们解释了该软件的结构及其关键功能，并演示了如何使用它来加速研究。有了SwarmRL，我们的目标是简化对微型机器人控制的研究，同时弥合实验和仿真驱动科学之间的差距。SwarmRL在GitHub上的开源地址是https://github.com/SwarmRL/SwarmRL。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

SwarmRL: building the future of smart active systems

This work introduces SwarmRL, a Python package designed to study intelligent active particles. SwarmRL provides an easy-to-use interface for developing models to control microscopic colloids using classical control and deep reinforcement learning approaches. These models may be deployed in simulations or real-world environments under a common framework. We explain the structure of the software and its key features and demonstrate how it can be used to accelerate research. With SwarmRL, we aim to streamline research into micro-robotic control while bridging the gap between experimental and simulation-driven sciences. SwarmRL is available open-source on GitHub at https://github.com/SwarmRL/SwarmRL.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

The European Physical Journal E CHEMISTRY, PHYSICAL-MATERIALS SCIENCE, MULTIDISCIPLINARY

CiteScore

2.60

自引率

5.60%

发文量

审稿时长

3 months

期刊介绍： EPJ E publishes papers describing advances in the understanding of physical aspects of Soft, Liquid and Living Systems. Soft matter is a generic term for a large group of condensed, often heterogeneous systems -- often also called complex fluids -- that display a large response to weak external perturbations and that possess properties governed by slow internal dynamics. Flowing matter refers to all systems that can actually flow, from simple to multiphase liquids, from foams to granular matter. Living matter concerns the new physics that emerges from novel insights into the properties and behaviours of living systems. Furthermore, it aims at developing new concepts and quantitative approaches for the study of biological phenomena. Approaches from soft matter physics and statistical physics play a key role in this research. The journal includes reports of experimental, computational and theoretical studies and appeals to the broad interdisciplinary communities including physics, chemistry, biology, mathematics and materials science.