<ul><li>Active matter refers to systems composed of self-propelled entities that consume energy to produce motion, exhibiting complex non-equilibrium dynamics.</li><li>Reinforcement learning (RL) has emerged as a promising framework for addressing the complexities of active matter.</li><li>This review explores the integration of RL for guiding and controlling active matter systems, focusing on optimal motion strategies for individual active particles and regulation of collective dynamics in active swarms.</li><li>The application of RL in active matter systems can advance the understanding, manipulation, and control of active matter in various fields such as biological systems, robotics, and medical science.</li></ul>

Reinforcement Learning for Active Matter

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