A Hitchhiker's Guide To Active Motion

TL;DR

This paper introduces a minimal model of an intelligent hitchhiking particle that uses reinforcement learning to navigate by attaching to active Brownian particles, outperforming bath particles in persistent motion.

Contribution

It develops a simple yet effective model of an IHP that can anticipate and adapt to the motion patterns of active particles using reinforcement learning.

Findings

IHP achieves persistent, long-time ballistic motion.

Performance depends on bath particle density and persistence time.

Analytic model explains mean-squared displacement behavior.

Abstract

Intelligent decisions in response to external informative input can allow organisms to achieve their biological goals while spending very little of their own resources. In this paper, we develop and study a minimal model for a navigational task, performed by an otherwise completely motorless particle that possesses the ability of \textit{hitchhiking} in a bath of active Brownian particles (ABPs). Hitchhiking refers to identifying and attaching to suitable surrounding bath particles. Using a reinforcement learning algorithm, such an agent, which we refer to as intelligent hitchhiking particle (IHP), is enabled to persistently navigate in the desired direction. This relatively simple IHP can also anticipate and react to characteristic motion patterns of their hosts, which we exemplify for a bath of chiral ABPs (cABPs). To demonstrate that the persistent motion of the IHP will outperform that of the bath particles in view of long-time ballistic motion, we calculate the mean-squared displacement and discuss its dependence on the density and persistence time of the bath ABPs by means of an analytic model.