Offline Reinforcement Learning for Fluid Controls: Data-based Multi-observational Policy Extraction

2026-06-29

AI Summary

This work addresses the high computational cost and limited flexibility of conventional online reinforcement learning in fluid control, which relies on extensive high-fidelity real-time interactions and requires retraining whenever sensor configurations change. To overcome these limitations, the authors propose an offline reinforcement learning framework that incorporates a sensor-position conditioning mechanism and a Point Attention layer, enabling a single policy network to generalize across arbitrary sensor layouts without retraining. The approach significantly enhances deployment adaptability while maintaining control performance. Its efficacy is demonstrated in both Kuramoto–Sivashinsky and Navier–Stokes simulation environments, where it achieves effective chaotic suppression and airfoil flow control, thereby offering a practical and efficient solution for optimizing sensor placement in fluid dynamic systems.

Abstract

Active flow control is a fundamental application in engineering. Recent advances in deep reinforcement learning have made progress in this field. However, the classical online RL approaches require extensive real-time interactions with the high fidelity environment, while each sensor configuration change necessitates whole policy retraining. All these factors result in prohibitive computational costs for real-world applications. In this work, we propose a novel offline RL framework that addresses both challenges through data-driven policy extraction. We develop a sensor position-conditioned architecture that enables a single policy network to adapt seamlessly to multiple sensor arrangements. The position-conditioned approach incorporated spatial relationship modeling through Point Attention layers to ensure the generalizability to varying sensor placements. We demonstrate the framework on two representative problems, mitigating chaoticity in the Kuramoto-Sivashinsky equation and flow control over airfoils governed by the Navier-Stokes equation. The result demonstrates that the policy extraction from the dataset provides unprecedented flexibility for sensor placement optimization. This approach represents a significant step towards adaptive, intelligent flow control systems.

Problem

• Offline Reinforcement Learning
• Fluid Control
• Sensor Configuration
• Policy Generalization
• Computational Cost

Innovation

• offline reinforcement learning
• sensor position-conditioned architecture
• Point Attention
• data-driven policy extraction
• fluid control