用强化学习控制自驱动粒子，让它们学会高效移动和协作。
Reinforcement Learning for Active Matter
- 用强化学习优化单个粒子的运动策略
- 实现群体自组织与目标导向的集体行为调控
- 适合机器人、生物系统等领域的研究者阅读
主动物质是由消耗能量产生运动的自驱动粒子组成的系统，表现出复杂的非平衡动力学，传统模型难以应对。随着机器学习快速发展，强化学习（RL）成为解决主动物质复杂性的有前景框架。本文系统介绍将强化学习用于引导和控制主动物质系统的进展，重点聚焦两个方面：个体主动粒子的最优运动策略，以及主动集群集体动力学的调控。讨论了利用强化学习优化个体粒子的导航、觅食和运动策略；同时分析了强化学习在调控集体行为中的应用，强调其在促进主动集群自组织与目标导向控制中的作用。该研究为深化对主动物质的理解、操控与控制提供了重要洞见，有望推动生物学系统、机器人学和医学科学等领域的发展。
原文摘要 · Abstract (English)
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.
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