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.
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