使用强化学习在复杂运动景观中导航的活性粒子
Active particles using reinforcement learning to navigate in complex motility landscapes
摘要：
随着最小技术的长度尺度不断超越微米尺度，为机器人组件配备智能和自主决策的手段变得越来越重要。在表格Q学习算法的帮助下，我们设计了一个用于训练微型游泳者的模型，以快速浏览由各种不同标量运动场给出的环境，同时接收有限的本地信息。我们比较的microswimmer的性能，定义通过时间的第一次通过到一个目标，与合适的参考情况下的性能。我们表明，用我们的强化学习模型获得的策略确实代表了一种有效的导航策略，其性能优于参考案例。通过面对游泳运动员与各种不熟悉的环境后，最终的培训，我们表明，所获得的战略推广到不同类别的随机场场。
英文摘要：
As the length scales of the smallest technology continue to advance beyond the micron scale it becomes increasingly important to equip robotic components with the means for intelligent and autonomous decision making with limited information. With the help of a tabular Q-learning algorithm, we design a model for training a microswimmer, to navigate quickly through an environment given by various different scalar motility fields, while receiving a limited amount of local information. We compare the performances of the microswimmer, defined via time of first passage to a target, with performances of suitable reference cases. We show that the strategy obtained with our reinforcement learning model indeed represents an efficient navigation strategy, that outperforms the reference cases. By confronting the swimmer with a variety of unfamiliar environments after the finalised training, we show that the obtained strategy generalises to different classes of random fields.
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