Multi-fidelity reinforcement learning framework for shape optimization

TL;DR

This paper introduces a multi-fidelity reinforcement learning framework that reduces computational costs and enhances policy generalization for complex shape optimization tasks, demonstrated on airfoil design at high Reynolds numbers.

Contribution

It presents a novel transfer learning approach leveraging multi-fidelity simulations to improve DRL efficiency and generalization in scientific optimization problems.

Findings

Reduced computational costs by over 30%

Enabled policy learning across multiple fidelity environments

Promoted policy exploration and prevented over-fitting

Abstract

Deep reinforcement learning (DRL) is a promising outer-loop intelligence paradigm which can deploy problem solving strategies for complex tasks. Consequently, DRL has been utilized for several scientific applications, specifically in cases where classical optimization or control methods are limited. One key limitation of conventional DRL methods is their episode-hungry nature which proves to be a bottleneck for tasks which involve costly evaluations of a numerical forward model. In this article, we address this limitation of DRL by introducing a controlled transfer learning framework that leverages a multi-fidelity simulation setting. Our strategy is deployed for an airfoil shape optimization problem at high Reynolds numbers, where our framework can learn an optimal policy for generating efficient airfoil shapes by gathering knowledge from multi-fidelity environments and reduces computational costs by over 30\%. Furthermore, our formulation promotes policy exploration and generalization to new environments, thereby preventing over-fitting to data from solely one fidelity. Our results demonstrate this framework's applicability to other scientific DRL scenarios where multi-fidelity environments can be used for policy learning.