Reinforcement Learning for Active Matter
Wenjie Cai, Gongyi Wang, Yu Zhang, Xiang Qu, Zihan Huang
活性物质是指由自走实体组成的系统,它们消耗能量产生运动,表现出复杂的非平衡动力学,挑战传统模型。 随着机器学习的快速发展,强化学习(RL)已成为解决活性物质复杂性的有前途的框架。 本综述系统地介绍了用于引导和控制活性物质系统的RL的集成,重点关注两个关键方面:单个活性粒子的最佳运动策略和活性群中集体动力学的调节。 我们讨论使用RL来优化单个活性粒子的导航,觅食和运动策略。 此外,还研究了RL在调节集体行为中的应用,强调其在促进主动群体的自我组织和目标导向控制方面的作用。 这项研究为RL如何推进对活性物质的理解,操纵和控制提供了宝贵的见解,为生物系统,机器人和医学等领域的未来发展铺平了道路。
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 mo...
