  Equipping active colloidal robots with intelligence such that they can
efficiently navigate in unknown complex environments could dramatically impact
their use in emerging applications like precision surgery and targeted drug
delivery. Here we develop a model-free deep reinforcement learning that can
train colloidal robots to learn effective navigation strategies in unknown
environments with random obstacles. We show that trained robot agents learn to
make navigation decisions regarding both obstacle avoidance and travel time
minimization, based solely on local sensory inputs without prior knowledge of
the global environment. Such agents with biologically inspired mechanisms can
acquire competitive navigation capabilities in large-scale, complex
environments containing obstacles of diverse shapes, sizes, and configurations.
This study illustrates the potential of artificial intelligence in engineering
active colloidal systems for future applications and constructing complex
active systems with visual and learning capability.
