Reinforcement Learning for Active Matter
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914017897873408 |
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| author | Cai, Wenjie Wang, Gongyi Zhang, Yu Qu, Xiang Huang, Zihan |
| author_facet | Cai, Wenjie Wang, Gongyi Zhang, Yu Qu, Xiang Huang, Zihan |
| contents | Active matter refers to systems composed of self-propelled entities that consume energy to produce motion, exhibiting complex non-equilibrium dynamics that challenge traditional models. With the rapid advancements in machine learning, reinforcement learning (RL) has emerged as a promising framework for addressing the complexities of active matter. This review systematically introduces the integration of RL for guiding and controlling active matter systems, focusing on two key aspects: optimal motion strategies for individual active particles and the regulation of collective dynamics in active swarms. We discuss the use of RL to optimize the navigation, foraging, and locomotion strategies for individual active particles. In addition, the application of RL in regulating collective behaviors is also examined, emphasizing its role in facilitating the self-organization and goal-directed control of active swarms. This investigation offers valuable insights into how RL can advance the understanding, manipulation, and control of active matter, paving the way for future developments in fields such as biological systems, robotics, and medical science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23308 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning for Active Matter Cai, Wenjie Wang, Gongyi Zhang, Yu Qu, Xiang Huang, Zihan Soft Condensed Matter Machine Learning Robotics Biological Physics Active matter refers to systems composed of self-propelled entities that consume energy to produce motion, exhibiting complex non-equilibrium dynamics that challenge traditional models. With the rapid advancements in machine learning, reinforcement learning (RL) has emerged as a promising framework for addressing the complexities of active matter. This review systematically introduces the integration of RL for guiding and controlling active matter systems, focusing on two key aspects: optimal motion strategies for individual active particles and the regulation of collective dynamics in active swarms. We discuss the use of RL to optimize the navigation, foraging, and locomotion strategies for individual active particles. In addition, the application of RL in regulating collective behaviors is also examined, emphasizing its role in facilitating the self-organization and goal-directed control of active swarms. This investigation offers valuable insights into how RL can advance the understanding, manipulation, and control of active matter, paving the way for future developments in fields such as biological systems, robotics, and medical science. |
| title | Reinforcement Learning for Active Matter |
| topic | Soft Condensed Matter Machine Learning Robotics Biological Physics |
| url | https://arxiv.org/abs/2503.23308 |