We formulate the theory for steering an active particle with optimal travel time between two locations and apply it to the Mexican hat potential without brim. For small heights the particle can cross the potential barrier, while for large heights it has to move around it. Thermal fluctuations in the orientation strongly affect the path over the barrier. Then we consider a smart active particle and apply reinforcement learning. We show how the active particle learns in repeating episodes to move optimally. The optimal steering is stored in the optimized action-value function, which is able to rectify thermal fluctuations.
Steering Active-Colloid Assembly by Biasing Dissipation
University of Science and Technology of China（中国科学技术大学）；Rice University（莱斯大学）；Hefei National Research Center for Physical Sciences at the Microscale（合肥微尺度物质科学国家研究中心）
Binary Mixtures of Intelligent Active Brownian Particles with Visual Perception
Theoretical Physics of Living Matter, Institute for Advanced Simulation, Forschungszentrum Jülich（于利希研究中心高级模拟所理论生命物理）；Department of Physics, Syracuse University（锡拉丘兹大学物理系）
Nonequilibrium energetics of sensing and actuation by a smart active particle
University of Cambridge（剑桥大学）；Max Planck Institute for Dynamics and Self-Organization (MPIDS)（马克斯·普朗克动力学与自组织研究所）；University of Rome “Tor Vergata”（罗马托维尔加塔大学）
