  We propose a new low-cost machine-learning-based methodology which assists
designers in reducing the gap between the problem and the solution in the
design process. Our work applies reinforcement learning (RL) to find the
optimal task-oriented design solution through the construction of the design
action for each task. For this task-oriented design, the 3D design process in
product design is assigned to an action space in Deep RL, and the desired 3D
model is obtained by training each design action according to the task. By
showing that this method achieves satisfactory design even when applied to a
task pursuing multiple goals, we suggest the direction of how machine learning
can contribute to the design process. Also, we have validated with product
designers that this methodology can assist the creative part in the process of
design.
