  Efficient navigation and precise localization of Brownian micro/nano
self-propelled motor particles within complex landscapes could enable future
high-tech applications involving for example drug delivery, precision surgery,
oil recovery, and environmental remediation. Here we employ a model-free deep
reinforcement learning algorithm based on bio-inspired neural networks to
enable different types of micro/nano motors to be continuously controlled to
carry out complex navigation and localization tasks. Micro/nano motors with
either tunable self-propelling speeds or orientations or both, are found to
exhibit strikingly different dynamics. In particular, distinct control
strategies are required to achieve effective navigation in free space and
obstacle environments, as well as under time constraints. Our findings provide
fundamental insights into active dynamics of Brownian particles controlled
using artificial intelligence and could guide the design of motor and robot
control systems with diverse application requirements.
