2024/06/05 by Christopher Koh, Koh, Christopher, Laurent Pagnier +3 · 2 citations
Computer Science · Economics, Econometrics and Finance · #Chaotic Dynamics (nlin.CD) #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Reservoir Computing #Reinforcement Learning in Robotics #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2406.10242
openalex publication_date 2024/06/05 · openalex created_date 2024/06/19 · openalex updated_date 2026/07/28
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.