Multi-fidelity reinforcement learning framework for shape optimization
This addresses a bottleneck for scientific applications of DRL where costly simulations limit efficiency, though it is incremental as it builds on existing DRL methods.
The paper tackles the high computational cost of deep reinforcement learning (DRL) in tasks with expensive evaluations by introducing a multi-fidelity transfer learning framework, applied to airfoil shape optimization at high Reynolds numbers, reducing computational costs by over 30%.
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.