Finding Efficient Swimming Strategies in a Three Dimensional Chaotic Flow by Reinforcement Learning

TL;DR

This paper demonstrates how reinforcement learning enables microswimmers to develop effective navigation strategies in complex three-dimensional chaotic flows, improving their ability to move upward and escape fluid traps.

Contribution

The study introduces a reinforcement learning framework for microswimmers with limited actions to learn optimal navigation in chaotic 3D flows, showcasing its effectiveness.

Findings

Reinforcement learning enables microswimmers to find efficient upward movement strategies.

Limited action sets are sufficient for complex navigation tasks.

The approach effectively escapes local fluid traps in chaotic flows.

Abstract

We apply a reinforcement learning algorithm to show how smart particles can learn approximately optimal strategies to navigate in complex flows. In this paper we consider microswimmers in a paradigmatic three-dimensional case given by a stationary superposition of two Arnold-Beltrami-Childress flows with chaotic advection along streamlines. In such a flow, we study the evolution of point-like particles which can decide in which direction to swim, while keeping the velocity amplitude constant. We show that it is sufficient to endow the swimmers with a very restricted set of actions (six fixed swimming directions in our case) to have enough freedom to find efficient strategies to move upward and escape local fluid traps. The key ingredient is the learning-from-experience structure of the algorithm, which assigns positive or negative rewards depending on whether the taken action is, or is not, profitable for the predetermined goal in the long term horizon. This is another example supporting the efficiency of the reinforcement learning approach to learn how to accomplish difficult tasks in complex fluid environments.