The question of how "smart" active agents, like insects, microorganisms, or future colloidal robots need to steer to optimally reach or discover a target, such as an odor source, food, or a cancer cell in a complex environment has recently attracted great interest. Here, we provide an overview of recent developments, regarding such optimal navigation problems, from the micro- to the macroscale, and give a perspective by discussing some of the challenges which are ahead of us. Besides exemplifying an elementary approach to optimal navigation problems, the article focuses on works utilizing machine learning-based methods. Such learning-based approaches can uncover highly efficient navigation strategies even for problems that involve e.g. chaotic, high-dimensional, or unknown environments and are hardly solvable based on conventional analytical or simulation methods.

翻译：关于“智能”活性剂（如昆虫、微生物或未来的胶体机器人）如何在复杂环境中通过转向最优地到达或发现目标（如气味源、食物或癌细胞）的问题，近期引起了极大兴趣。本文从微观到宏观尺度概述了此类最优导航问题的最新进展，并通过讨论我们面临的部分挑战提供了未来展望。除举例说明最优导航问题的基本处理方法外，本文重点介绍了基于机器学习方法的研究。此类基于学习的方法甚至能够揭示高效导航策略，可解决涉及混沌、高维或未知环境等传统分析或模拟方法难以处理的问题。