Reinforcement Learning of Artificial Microswimmers

TL;DR

This paper demonstrates the application of reinforcement learning to control artificial microswimmers, enabling adaptive navigation and collective behavior in real-world environments, bridging a gap in microscopic machine learning implementations.

Contribution

It introduces a hybrid reinforcement learning approach for microswimmers, incorporating real-time control and multi-agent information sharing, pioneering adaptive behavior in microscopic systems.

Findings

Successful navigation in a grid world environment

Brownian motion noise influences learning and actions

Extended learning to multiple microswimmers with shared information

Abstract

The behavior of living systems is based on the experience they gained through their interactions with the environment [1]. This experience is stored in the complex biochemical networks of cells and organisms to provide a relationship between a sensed situation and what to do in this situation [2-4]. An implementation of such processes in artificial systems has been achieved through different machine learning algorithms [5, 6]. However, for microscopic systems such as artificial microswimmers which mimic propulsion as one of the basic functionalities of living systems [7, 8] such adaptive behavior and learning processes have not been implemented so far. Here we introduce machine learning algorithms to the motion of artificial microswimmers with a hybrid approach. We employ self-thermophoretic artificial microswimmers in a real world environment [9, 10] which are controlled by a real-time microscopy system to introduce reinforcement learning [11-13]. We demonstrate the solution of a standard problem of reinforcement learning - the navigation in a grid world. Due to the size of the microswimmer, noise introduced by Brownian motion if found to contribute considerably to both the learning process and the actions within a learned behavior. We extend the learning process to multiple swimmers and sharing of information. Our work represents a first step towards the integration of learning strategies into microsystems and provides a platform for the study of the emergence of adaptive and collective behavior.