Active particles using reinforcement learning to navigate in complex motility landscapes

TL;DR

This paper develops a reinforcement learning-based model for microswimmers to navigate complex motility landscapes efficiently, demonstrating improved performance and generalization across various environments.

Contribution

It introduces a Q-learning approach for microswimmer navigation, showing enhanced efficiency and adaptability in complex motility fields compared to traditional methods.

Findings

Reinforcement learning improves navigation speed.

The strategy generalizes to different random fields.

Outperforms reference navigation strategies.

Abstract

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.