A review on deep reinforcement learning for fluid mechanics: an update

Abstract

In the past couple of years, the interest of the fluid mechanics community for deep reinforcement learning (DRL) techniques has increased at fast pace, leading to a growing bibliography on the topic. While the capabilities of DRL to solve complex decision-making problems make it a valuable tool for active flow control, recent publications also demonstrated applications to other fields, such as shape optimization or microfluidics. The present work aims at proposing an exhaustive review of the existing literature, and is a follow-up to our previous review on the topic. The contributions are regrouped by field of application, and are compared together regarding algorithmic and technical choices, such as state selection, reward design, time granularity, and more. Based on these comparisons, general conclusions are drawn regarding the current state-of-the-art in the domain, and perspectives for future improvements are sketched.

References in corpus (16)

• A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
• Challenges of Real-World Reinforcement Learning
• An Actor-Critic Algorithm for Sequence Prediction
• Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning
• Reinforcement-learning-based control of confined cylinder wakes with stability analyses
• DRLinFluids -- An open-source python platform of coupling Deep Reinforcement Learning and OpenFOAM
• Comparative analysis of machine learning methods for active flow control
• Deep reinforcement learning for large-eddy simulation modeling in wall-bounded turbulence
• Machine learning flow control with few sensor feedback and measurement noise
• Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning
• Maximum Entropy RL (Provably) Solves Some Robust RL Problems
• An application of data driven reward of deep reinforcement learning by dynamic mode decomposition in active flow control
• Flow Rate Control in Smart District Heating Systems Using Deep Reinforcement Learning
• Deep reinforcement learning for the control of conjugate heat transfer with application to workpiece cooling
• Evaluating the progress of Deep Reinforcement Learning in the real world: aligning domain-agnostic and domain-specific research
• Deep Reinforcement Learning for Active Flow Control around a Circular Cylinder Using Unsteady-mode Plasma Actuators

Cited by in corpus (10)

• Recent advances in applying deep reinforcement learning for flow control: perspectives and future directions
• Reinforcement-learning-based control of confined cylinder wakes with stability analyses
• The Road to the Ideal Stent: A Review of Stent Design Optimisation Methods, Findings, and Opportunities
• Multi-condition multi-objective optimization using deep reinforcement learning
• Reinforcement-learning-based control of convectively-unstable flows
• Discovering explicit Reynolds-averaged turbulence closures for turbulent separated flows through deep learning-based symbolic regression with non-linear corrections
• Investigation of reinforcement learning for shape optimization of profile extrusion dies
• How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learning
• Deep reinforcement learning for tracking a moving target in jellyfish-like swimming
• Integral modelling and Reinforcement Learning control of 3D liquid metal coating on a moving substrate