  Atomic-scale manipulation in scanning tunneling microscopy has enabled the
creation of quantum states of matter based on artificial structures and extreme
miniaturization of computational circuitry based on individual atoms. The
ability to autonomously arrange atomic structures with precision will enable
the scaling up of nanoscale fabrication and expand the range of artificial
structures hosting exotic quantum states. However, the \textit{a priori}
unknown manipulation parameters, the possibility of spontaneous tip apex
changes, and the difficulty of modeling tip-atom interactions make it
challenging to select manipulation parameters that can achieve atomic precision
throughout extended operations. Here we use deep reinforcement learning (DRL)
to control the real-world atom manipulation process. Several state-of-the-art
reinforcement learning techniques are used jointly to boost data efficiency.
The reinforcement learning agent learns to manipulate Ag adatoms on Ag(111)
surfaces with optimal precision and is integrated with path planning algorithms
to complete an autonomous atomic assembly system. The results demonstrate that
state-of-the-art deep reinforcement learning can offer effective solutions to
real-world challenges in nanofabrication and powerful approaches to
increasingly complex scientific experiments at the atomic scale.
