Physics-Guided Actor-Critic Reinforcement Learning for Swimming in Turbulence

TL;DR

This paper introduces a physics-guided reinforcement learning method called actor-physicist, which effectively controls particles in turbulent flows by integrating physical heuristics into the learning process, outperforming standard methods.

Contribution

The paper proposes a novel physics-informed reinforcement learning framework that combines analytical physical heuristics with neural network-based actor-critic algorithms for particle control in turbulence.

Findings

The actor-physicist method outperforms physics-agnostic reinforcement learning in turbulent flow control.

The approach is validated in synthetic and realistic flow environments.

Physics-informed RL reduces control effort and improves particle proximity maintenance.

Abstract

Turbulent diffusion causes particles placed in proximity to separate. We investigate the required swimming efforts to maintain an active particle close to its passively advected counterpart. We explore optimally balancing these efforts by developing a novel physics-informed reinforcement learning strategy and comparing it with prescribed control and physics-agnostic reinforcement learning strategies. Our scheme, coined the actor-physicist, is an adaptation of the actor-critic algorithm in which the neural network parameterized critic is replaced with an analytically derived physical heuristic function, the physicist. We validate the proposed physics-informed reinforcement learning approach through extensive numerical experiments in both synthetic BK and more realistic Arnold-Beltrami-Childress flow environments, demonstrating its superiority in controlling particle dynamics when compared to standard reinforcement learning methods.