  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.
