Artificial microswimmers that can replicate the complex behavior of active matter are often designed to mimic the self-propulsion of microscopic living organisms. However, compared with their living counterparts, artificial microswimmers have a limited ability to adapt to environmental signals or to retain a physical memory to yield optimized emergent behavior. Different from macroscopic living systems and robots, both microscopic living organisms and artificial microswimmers are subject to Brownian motion, which randomizes their position and propulsion direction. Here, we combine real-world artificial active particles with machine learning algorithms to explore their adaptive behavior in a noisy environment with reinforcement learning. We use a real-time control of self-thermophoretic active particles to demonstrate the solution of a simple standard navigation problem under the inevitable influence of Brownian motion at these length scales. We show that, with external control, collective learning is possible. Concerning the learning under noise, we find that noise decreases the learning speed, modifies the optimal behavior, and also increases the strength of the decisions made. As a consequence of time delay in the feedback loop controlling the particles, an optimum velocity, reminiscent of optimal run-and-tumble times of bacteria, is found for the system, which is conjectured to be a universal property of systems exhibiting delayed response in a noisy environment.

可以复制活性物质复杂行为的人工微型游泳者通常旨在模仿微观生物的自我推进。然而，与活着的同类相比，人工微型游泳者适应环境信号或保留物理记忆以产生优化的紧急行为的能力有限。与宏观生命系统和机器人不同，微观生物和人工微型游泳者都受到布朗运动的影响，这使得它们的位置和推进方向随机化。在这里，我们将真实世界的人工活性粒子与机器学习算法相结合，通过强化学习探索它们在嘈杂环境中的自适应行为。我们使用自热泳活性粒子的实时控制来演示在这些长度尺度上布朗运动不可避免的影响下的简单标准导航问题的解决方案。我们表明，通过外部控制，集体学习是可能的。关于噪声下的学习，我们发现噪声会降低学习速度，修改最佳行为，并增加所做决策的强度。由于控制粒子的反馈回路中的时间延迟，为系统找到了最佳速度，让人联想到细菌的最佳运行和翻滚时间，据推测这是系统的普遍属性，在嘈杂的环境。通过外部控制，集体学习成为可能。关于噪声下的学习，我们发现噪声会降低学习速度，修改最佳行为，并增加所做决策的强度。由于控制粒子的反馈回路中的时间延迟，为系统找到了最佳速度，让人联想到细菌的最佳运行和翻滚时间，据推测这是系统的普遍属性，在嘈杂的环境。通过外部控制，集体学习成为可能。关于噪声下的学习，我们发现噪声会降低学习速度，修改最佳行为，并增加所做决策的强度。由于控制粒子的反馈回路中的时间延迟，为系统找到了最佳速度，让人联想到细菌的最佳运行和翻滚时间，据推测这是系统的普遍属性，在嘈杂的环境。