# Design Strategy Network: A deep hierarchical framework to represent generative design strategies in complex action spaces

arXiv: https://arxiv.org/abs/2110.03760 · HTML: https://mlsanity.com/paper/2110.03760/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 73 references that are in the map.
- First posted on arXiv 7 Oct 2021.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Image Transformer](https://mlsanity.com/paper/1802.05751/) (2018)
- [Frustum PointNets for 3D Object Detection from RGB-D Data](https://mlsanity.com/paper/1711.08488/) (2017)
- [Recurrent Models of Visual Attention](https://mlsanity.com/paper/1406.6247/) (2014)
- [Thinking Fast and Slow with Deep Learning and Tree Search](https://mlsanity.com/paper/1705.08439/) (2017)
- [Towards Interpretable Reinforcement Learning Using Attention Augmented Agents](https://mlsanity.com/paper/1906.02500/) (2019)
- [In the Eye of the Beholder: Gaze and Actions in First Person Video](https://mlsanity.com/paper/2006.00626/) (2020)
- [Deep Reinforcement Learning in Parameterized Action Space](https://mlsanity.com/paper/1511.04143/) (2015)
- [Reinforcement Learning with Parameterized Actions](https://mlsanity.com/paper/1509.01644/) (2015)
- [Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space](https://mlsanity.com/paper/1810.06394/) (2018)
- [Discrete and Continuous Action Representation for Practical RL in Video Games](https://mlsanity.com/paper/1912.11077/) (2019)
- [3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders](https://mlsanity.com/paper/1904.07964/) (2019)
- [On Deep Set Learning and the Choice of Aggregations](https://mlsanity.com/paper/1903.07348/) (2019)
- [Attention Routing: track-assignment detailed routing using attention-based reinforcement learning](https://mlsanity.com/paper/2004.09473/) (2020)
- [Gaze Training by Modulated Dropout Improves Imitation Learning](https://mlsanity.com/paper/1904.08377/) (2019)
- [Learning to design from humans: Imitating human designers through deep learning](https://mlsanity.com/paper/1907.11813/) (2019)
- [Learning an Optimization Algorithm through Human Design Iterations](https://mlsanity.com/paper/1608.06984/) (2016)

## Built on by (most cited first, 4 of 4)

- [Conceptual Design Generation Using Large Language Models](https://mlsanity.com/paper/2306.01779/) (2023)
- [Generative AI and Process Systems Engineering: The Next Frontier](https://mlsanity.com/paper/2402.10977/) (2024)
- [Opportunities for Large Language Models and Discourse in Engineering Design](https://mlsanity.com/paper/2306.09169/) (2023)
- [Learning to design without prior data: Discovering generalizable design strategies using deep learning and tree search](https://mlsanity.com/paper/2211.15068/) (2022)

## Abstract (arXiv)

Generative design problems often encompass complex action spaces that may be divergent over time, contain state-dependent constraints, or involve hybrid (discrete and continuous) domains. To address those challenges, this work introduces Design Strategy Network (DSN), a data-driven deep hierarchical framework that can learn strategies over these arbitrary complex action spaces. The hierarchical architecture decomposes every action decision into first predicting a preferred spatial region in the design space and then outputting a probability distribution over a set of possible actions from that region. This framework comprises a convolutional encoder to work with image-based design state representations, a multi-layer perceptron to predict a spatial region, and a weight-sharing network to generate a probability distribution over unordered set-based inputs of feasible actions. Applied to a truss design study, the framework learns to predict the actions of human designers in the study, capturing their truss generation strategies in the process. Results show that DSNs significantly outperform non-hierarchical methods of policy representation, demonstrating their superiority in complex action space problems.