Optimization and passive flow control using single-step deep reinforcement learning

4 Jun 2020 H. Ghraieb J. Viquerat A. Larcher P. Meliga E. Hachem

This research gauges the ability of deep reinforcement learning (DRL) techniques to assist the optimization and control of fluid mechanical systems. It combines a novel, "degenerate" version of the proximal policy optimization (PPO) algorithm, that trains a neural network in optimizing the system only once per learning episode, and an in-house stabilized finite elements environment implementing the variational multiscale (VMS) method, that computes the numerical reward fed to the neural network... (read more)

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Methods used in the Paper

Entropy Regularization
Policy Gradient Methods