Emergence of odd elasticity in a microswimmer using deep reinforcement learning

TL;DR

This paper demonstrates how deep reinforcement learning can optimize the locomotion of elastic microswimmers, revealing emergent odd elasticity linked to their cyclic deformation dynamics.

Contribution

It introduces a novel machine learning approach to optimize microswimmer dynamics and extracts physical quantities like odd elasticity from learned behaviors.

Findings

Optimized microswimmer locomotion via deep Q-Networks.

Emergent odd elasticity correlates with cyclic deformation frequency.

Performance is proportional to loop area and frequency.

Abstract

We use the Deep Q-Network with reinforcement learning to investigate the emergence of odd elasticity in an elastic microswimmer model. For an elastic microswimmer, it is challenging to obtain the optimized dynamics due to the intricate elastohydrodynamic interactions. However, our machine-trained model adopts a novel transition strategy (the waiting behavior) to optimize the locomotion. For the trained microswimmers, we evaluate the performance of the cycles by the product of the loop area (called non-reciprocality) and the loop frequency, and show that the average swimming velocity is proportional to the performance. By calculating the force-displacement correlations, we obtain the effective odd elasticity of the microswimmer to characterize its non-reciprocal dynamics. This emergent odd elasticity is shown to be closely related to the loop frequency of the cyclic deformation. Our work demonstrates the utility of machine learning in achieving optimal dynamics for elastic microswimmers and introduces post-analysis methods to extract crucial physical quantities such as non-reciprocality and odd elasticity.