Authors: Seowoo Jang, Soyoung Yoo, Namwoo Kang

You can find this paper at Generative Design by Reinforcement Learning

Paper: Generative Design by Reinforcement Learning – Jang, Yoo, Kang 1/1

Technical Aspectslink copied

Software & Plug-ins

 TopOpNet (topology optimization)

Summary

In the framework of generative design, the paper proposes a RL- based method to enhance the design diversity of topology optimization outcomes. Particularly, the problem is formulated as a sequence of defining the optimal design parameter combinations in reference to a given initial design.

The RL framework that is implemented is Proximal Policy optimization, while, in order to enhance its feature extracting capability and accelerate the training process, a Variational Autoencoder regularizer is also added to it. The design variation is considered into the rewarding function through pixel difference and structure dissimilarity. Comparing the latter, it is concluded that pixel difference is a more adequate rewarding metric for the given problem.

Project Information

Author(s): Seowoo Jang, Soyoung Yoo, Namwoo Kang

Year: 2022

Project type: Paper

Keywords: Reinforcement Learning, Policy Iteration, Proximal Policy optimization, Variational Autoencoders

Topic tags: Generative design, Generative Deep Learning, Topology Optimization, Data Augmentation