Optimal steering of a smart active particle

TL;DR

This paper develops a theory for optimally steering active particles between locations, incorporating thermal fluctuations and reinforcement learning, to improve travel time efficiency in complex potentials.

Contribution

It introduces a novel optimal steering framework for active particles, combining theoretical modeling with reinforcement learning to adaptively improve navigation.

Findings

Optimal steering reduces travel time in complex potentials.

Reinforcement learning enables particles to adaptively learn efficient paths.

Thermal fluctuations are effectively managed by the learned control policy.

Abstract

We formulate the theory for steering an active particle with optimal travel time between two locations and apply it to the Mexican hat potential without brim. For small heights the particle can cross the potential barrier, while for large heights it has to move around it. Thermal fluctuations in the orientation strongly affect the path over the barrier. Then we consider a smart active particle and apply reinforcement learning. We show how the active particle learns in repeating episodes to move optimally. The optimal steering is stored in the optimized action-value function, which is able to rectify thermal fluctuations.