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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METHOD TYPE
Entropy Regularization
Regularization
PPO
Policy Gradient Methods